happiness index research articles

Happiness and Life Satisfaction

Self-reported life satisfaction differs widely between people and between countries. What explains these differences?

By Esteban Ortiz-Ospina and Max Roser

First published in 2013; most recent substantive revision February 2024.

How happy are people today? Were people happier in the past? How satisfied with their lives are people in different societies? And how do our living conditions affect all of this?

These are difficult questions to answer, but they are questions that undoubtedly matter for each of us personally. Indeed, today, life satisfaction and happiness are central research areas in the social sciences, including in ‘mainstream’ economics.

Social scientists often recommend that measures of subjective well-being should augment the usual measures of economic prosperity, such as GDP per capita . 1 But how can happiness be measured? Are there reliable comparisons of happiness across time and space that can give us clues regarding what makes people declare themselves ‘happy’?

In this topic page, we discuss the data and empirical evidence that might answer these questions. Our focus here will be on survey-based measures of self-reported happiness and life satisfaction. Here is a preview of what the data reveals.

• Surveys asking people about life satisfaction and happiness do measure subjective well-being with reasonable accuracy.
• Life satisfaction and happiness vary widely both within and among countries. It only takes a glimpse at the data to see that people are distributed along a wide spectrum of happiness levels.
• Richer people tend to say they are happier than poorer people; richer countries tend to have higher average happiness levels; and across time, most countries that have experienced sustained economic growth have seen increasing happiness levels. So, the evidence suggests that income and life satisfaction tend to go together (which still doesn’t mean they are one and the same).
• Important life events such as marriage or divorce do affect our happiness but have surprisingly little long-term impact. The evidence suggests that people tend to adapt to changes.

See all interactive charts on Happiness and Life Satisfaction ↓

Other research and writing on happiness and life satisfaction on Our World in Data:

• Are Facebook and other social media platforms bad for our well-being?
• Are people more likely to be lonely in so-called 'individualistic' societies?
• Are we happier when we spend more time with others?
• Collective pessimism and our inability to guess the happiness of others
• How important are social relations for our health and well-being?
• Is there a loneliness epidemic?
• There is a 'happiness gap' between East and West Germany

Happiness across the world today

The World Happiness Report is a well-known source of cross-country data and research on self-reported life satisfaction. The map here shows, country by country, the ‘happiness scores’ published this report.

The underlying source of the happiness scores in the World Happiness Report is the Gallup World Poll —a set of nationally representative surveys undertaken in more than 160 countries in over 140 languages.

The main life evaluation question asked in the poll is: “Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you, and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?” (This is also known as the “Cantril Ladder”.)

The map plots the average answer that survey respondents provided to this question in different countries. As with the steps of the ladder, values in the map range from 0 to 10.

There are large differences across countries. According to the most recent figures, European countries top the ranking: Finland, Denmark, Iceland, Switzerland, and the Netherlands have the highest scores. In the same year, the lowest national scores correspond to Afghanistan, South Sudan, and other countries in central Sub-Saharan Africa.

You can click on any country on the map to plot time series for specific countries.

Self-reported life satisfaction tends to correlate with other measures of well-being—richer and healthier countries tend to have higher average happiness scores. (More on this in the section below .)

Happiness over time

Findings from the integrated values surveys.

In addition to the Gallup World Poll (discussed above), the Integrated Values Surveys provides cross-country data on self-reported life satisfaction. These are the longest available time series of cross-country happiness estimates that include non-European nations.

The Integrated Values Surveys collect data from a series of representative national surveys covering almost 100 countries, with the earliest estimates dating back to 1981. In these surveys, respondents are asked: “Taking all things together, would you say you are (i) Very happy, (ii) Rather happy, (iii) Not very happy, or (iv) Not at all happy”. This visualization plots the share of people answering they are “very happy” or “rather happy”.

As we can see, in most countries, the trend is positive: In most countries with data from two or more surveys, the most recent observation is higher than the earliest. In some cases, the improvement has been very large; in Albania, for example, the share of people who reported being ‘very happy’ or ‘rather happy’ went from 33.4% in 1998 to 73.9% in 2022.

Findings from Eurobarometer

The Eurobarometer survey collects data on life satisfaction as part of their public opinion surveys. For several countries, these surveys have been conducted at least annually for more than 40 years. The visualization here shows the share of people who report being ‘very satisfied’ or ‘fairly satisfied’ with their standards of living.

Two points are worth emphasizing. First, estimates of life satisfaction often fluctuate around trends. In France, for example, we can see that the overall trend is positive, yet there is a pattern of ups and downs. And second, despite fluctuations, decade-long trends have been generally positive for most European countries.

In most cases, the share of people who say they are ‘very satisfied’ or ‘fairly satisfied’ with their life has gone up over the full survey period. 2 Yet there are some clear exceptions, of which Greece is the most notable example. In 2007, around 67% of the Greeks said they were satisfied with their life, but five years later, after the financial crisis struck, the corresponding figure was down to 32.4%. Despite recent improvements, Greeks today are, on average, much less satisfied with their lives than they were before the financial crisis. No other European country in this dataset has gone through a comparable negative shock.

The distribution of life satisfaction

More than averages — the distribution of life satisfaction scores.

Most of the studies comparing happiness and life satisfaction among countries focus on averages. However, distributional differences are also important.

Life satisfaction is often reported on a scale from 0 to 10, with 10 representing the highest possible level of satisfaction. This is the so-called ‘Cantril Ladder’. This visualization shows how responses are distributed across steps in this ladder. In each case, the height of the bars is proportional to the fraction of answers at each score. Each differently-colored distribution refers to a world region, and for each region, we have overlaid the distribution for the entire world as a reference.

These plots show that in Sub-Saharan Africa—the region with the lowest average scores—the distributions are consistently to the left of those in Europe.

This means that the share of people who are ‘happy’ is lower in Sub-Saharan Africa than in Western Europe, independently of which score in the ladder we use as a threshold to define ‘happy’. Similar comparisons can be made by contrasting other regions with high average scores (e.g., North America, Australia, and New Zealand) against those with low average scores (e.g., South Asia).

Another important point to notice is that the distribution of self-reported life satisfaction in Latin America and the Caribbean is high across the board—it is consistently to the right of other regions with roughly comparable income levels, such as Central and Eastern Europe.

This is part of a broader pattern: Latin American countries tend to have a higher subjective well-being than other countries with comparable levels of economic development. As we will see in the section on the social environment , culture, and history matter for self-reported life satisfaction.

If you want data on country-level distributions of scores, the Pew Global Attitudes Survey provides such figures for more than 40 countries.

Differences in happiness within countries

Happiness inequality, happiness inequality in the us and other rich countries.

The General Social Survey (GSS) in the US is a survey administered to a nationally representative sample of about 1,500 respondents each year since 1972 and is an important source of information on long-run trends of self-reported life satisfaction in the country. 3

Using this source, Stevenson and Wolfers (2008) 4 show that while the national average has remained broadly constant, inequality in happiness has fallen substantially in the US in recent decades.

The authors further note that this is true both when we think about inequality in terms of the dispersion of answers, and also when we think about inequality in terms of gaps between demographic groups. They note that two-thirds of the black-white happiness gap has been eroded (although today, white Americans remain happier on average, even after controlling for differences in education and income), and the gender happiness gap has disappeared entirely (women used to be slightly happier than men, but they are becoming less happy, and today there is no statistical difference once we control for other characteristics). 5

The results from Stevenson and Wolfers are consistent with other studies looking at changes of happiness inequality (or life satisfaction inequality) over time. In particular, researchers have noted that there is a correlation between economic growth and reductions in happiness inequality—even when income inequality is increasing at the same time. The visualization here uses data from Clark, Fleche, and Senik (2015) 6 shows this. It plots the evolution of happiness inequality within a selection of rich countries that experienced uninterrupted GDP growth.

In this chart, happiness inequality is measured by the dispersion — specifically the standard deviation — of answers in the World Values Survey . As we can see, there is a broad negative trend. In their paper, the authors show that the trend is positive in countries with falling GDP.

Why could it be that happiness inequality falls with rising income inequality?

Clark, Fleche, and Senik argue that part of the reason is that the growth of national income allows for the greater provision of public goods, which in turn tightens the distribution of subjective well-being. This can still be consistent with growing income inequality since public goods such as better health affect incomes and well-being differently.

Another possibility is that economic growth in rich countries has translated into a more diverse society in terms of cultural expressions (e.g., through the emergence of alternative lifestyles), which has allowed people to converge in happiness even if they diverge in incomes, tastes, and consumption. Steven Quartz and Annette Asp explain this hypothesis in a New York Times article , discussing evidence from experimental psychology.

The link between happiness and income

The link across countries, higher national incomes go together with higher average life satisfaction.

If we compare life satisfaction reports from around the world at any given point in time, we immediately see that countries with higher average national incomes tend to have higher average life satisfaction scores. In other words, people in richer countries tend to report higher life satisfaction than people in poorer countries. The scatter plot here shows this.

Each dot in the visualization represents one country. The vertical position of the dots shows the national average self-reported life satisfaction in the Cantril Ladder (a scale ranging from 0-10 where 10 is the highest possible life satisfaction), while the horizontal position shows GDP per capita based on purchasing power parity (i.e., GDP per head after adjusting for inflation and cross-country price differences).

This correlation holds even if we control for other factors: Richer countries tend to have higher average self-reported life satisfaction than poorer countries that are comparable in terms of demographics and other measurable characteristics. You can read more about this in the World Happiness Report 2017 , specifically the discussion in Chapter 2.

As we show below, income and happiness also tend to go together within countries and across time .

The link within countries

Higher personal incomes go together with higher self-reported life satisfaction.

Above; we point out that richer countries tend to be happier than poorer countries. Here, we show that the same tends to be true within countries: richer people within a country tend to be happier than poorer people in the same country. The visualizations here show us this by looking at happiness by income quintiles.

Firstly, we show each country in individual panels: within each panel is a connected scatter plot for a specific country. This means that for each country, we observe a line joining five points: each point marks the average income within an income quintile (horizontal axis) against the average self-reported life satisfaction of people at that income quintile (vertical axis).

What does this visualization tell us? We see that in all cases, lines are upward sloping: people in higher income quintiles tend to have higher average life satisfaction. Yet in some countries, the lines are steep and linear (e.g., in Costa Rica, richer people are happier than poorer people across the whole income distribution), while in some countries, the lines are less steep and non-linear (e.g., the richest group of people in the Dominican Republic is as happy as the second-richest group).

In a second visualization, we present the same data, but instead of plotting each country separately, showing all countries in one grid.

The resulting connected scatter plot may be messy, resembling a ‘spaghetti’ chart, but it is helpful to confirm the overall pattern: despite kinks here and there, lines are, by and large, upward-sloping.

Looking across and within countries

A snapshot of the correlation between income and happiness—between and within countries.

Do income and happiness tend to go together? The visualization here shows that the answer to this question is yes, both within and across countries.

It may take a minute to wrap your head around this visualization, but once you do, you can see that it handily condenses the key information from the previous three charts into one.

To show the income-happiness correlation across countries, the chart plots the relationship between self-reported life satisfaction on the vertical axis and GDP per capita on the horizontal axis. Each country is an arrow on the grid, and the location of the arrow tells us the corresponding combination of average income and average happiness.

To show the income-happiness correlation within countries, each arrow has a slope corresponding to the correlation between household incomes and self-reported life satisfaction within that country. In other words, the slope of the arrow shows how strong the relationship between income and life satisfaction is within that country. ( This chart gives you a visual example of how the arrows were constructed for each country). 7

If an arrow points northeast, that means richer people tend to report higher life satisfaction than poorer people in the same country. If an arrow is flat (i.e., points east), that means rich people are, on average, just as happy as poorer people in the same country.

As we can see, there is a very clear pattern: richer countries tend to be happier than poorer countries (observations are lined up around an upward-sloping trend), and richer people within countries tend to be happier than poorer people in the same countries (arrows are consistently pointing northeast).

It’s important to note that the horizontal axis is measured on a logarithmic scale. The cross-country relationship we would observe on a linear scale would be different since, at high national income levels, slightly higher national incomes are associated with a smaller increase in average happiness than at low levels of national incomes. In other words, the cross-country relationship between income and happiness is not linear on income (it is ‘log-linear’). We use the logarithmic scale to highlight two key facts: (i) at no point in the global income distribution is the relationship flat, and (ii) a doubling of the average income is associated with roughly the same increase in the reported life satisfaction, irrespective of the position in the global distribution.

These findings have been explored in more detail in a number of recent academic studies. Importantly, the much-cited paper by Stevenson and Wolfers (2008) 8 shows that these correlations hold even after controlling for various country characteristics, such as the demographic composition of the population, and are robust to different sources of data and types of subjective well-being measures.

Economic growth and happiness

In the charts above, we show that there is robust evidence of a strong correlation between income and happiness across and within countries at fixed points in time. Here, we want to show that, while less strong, there is also a correlation between income and happiness across time. Or, put differently, as countries get richer, the population tends to report higher average life satisfaction.

The chart shown here uses data from the World Values Survey to plot the evolution of national average incomes and national average happiness over time. To be specific, this chart shows the share of people who say they are ‘very happy’ or ‘rather happy’ in the World Values Survey (vertical axis) against GDP per head (horizontal axis). Each country is drawn as a line joining the first and last available observations across all survey waves. 9

As we can see, countries that experience economic growth also tend to experience happiness growth across waves in the World Values Survey. This is a correlation that holds after controlling for other factors that also change over time (in this chart from Stevenson and Wolfers (2008), you can see how changes in GDP per capita compare to changes in life satisfaction after accounting for changes in demographic composition and other variables).

An important point to note here is that economic growth and happiness growth tend to go together on average . Some countries, in some periods, experience economic growth without increasing happiness. The experience of the US in recent decades is a case in point. These instances may seem paradoxical given the evidence—we explore this question in the following section.

The Easterlin Paradox

The observation that economic growth does not always go together with increasing life satisfaction was first made by Richard Easterlin in the 1970s. Since then, there has been much discussion over what came to be known as the ‘Easterlin Paradox’.

At the heart of the paradox was the fact that richer countries tend to have higher self-reported happiness, yet in some countries for which repeated surveys were available over the course of the 1970s, happiness was not increasing with rising national incomes. This combination of empirical findings was paradoxical because the cross-country evidence (countries with higher incomes tended to have higher self-reported happiness) did not, in some cases, fit the evidence over time (countries seemed not to get happier as national incomes increased).

Notably, Easterlin and other researchers relied on data from the US and Japan to support this seemingly perplexing observation. If we look closely at the data underpinning the trends in these two countries, however, these cases are not, in fact, paradoxical.

Let us begin with the case of Japan. There, the earliest available data on self-reported life satisfaction came from the so-called ‘Life in Nation surveys’, which date back to 1958. At first glance, this source suggests that mean life satisfaction remained flat over a period of spectacular economic growth (see, for example, this chart from Easterlin and Angelescu 2011). 10 Digging a bit deeper, however, we find that things are more complex.

Stevenson and Wolfers (2008) 8 show that the life satisfaction questions in the ‘Life in Nation surveys’ changed over time, making it difficult—if not impossible—to track changes in happiness over the full period. The visualization here splits the life satisfaction data from the surveys into sub-periods where the questions remained constant. As we can see, the data is not supportive of a paradox: the correlation between GDP and happiness growth in Japan is positive within comparable survey periods. The reason for the alleged paradox is, in fact mismeasurement of how happiness changed over time.

In the US, the explanation is different but can once again be traced to the underlying data. Specifically, if we look more closely at economic growth in the US over the recent decades, one fact looms large: growth has not benefitted the majority of people. Income inequality in the US is exceptionally high and has been on the rise in the last four decades, with incomes for the median household growing much more slowly than incomes for the top 10%. As a result, trends in aggregate life satisfaction should not be seen as paradoxical: the income and standard of living of the typical US citizen have not grown much in the last couple of decades. (You can read more about this in our page on inequality and incomes across the distribution .)

Health and life satisfaction

Life expectancy and life satisfaction.

Health is an important predictor of life satisfaction, both within and among countries. In this visualization, we provide evidence of the cross-country relationship.

Each dot in the scatterplot represents one country. The vertical position of the dots shows national life expectancy at birth, and the horizontal position shows the national average self-reported life satisfaction in the Cantril Ladder (a scale ranging from 0-10, where 10 is the highest possible life satisfaction).

As we can see, there is a strong positive correlation: countries where people tend to live longer are also countries where people tend to say more often that they are satisfied with their lives. A similar relationship holds for other health outcomes (e.g., life satisfaction tends to be higher in countries with lower child mortality ).

The relationship plotted in the chart clearly reflects more than just the link between health and happiness since countries with high life expectancy also tend to be countries with many other distinct characteristics. However, the positive correlation between life expectancy and life satisfaction remains after controlling for observable country characteristics, such as income and social protection. You can read more about this in the World Happiness Report 2017 , specifically the discussion in Chapter 2.

Life satisfaction through life events

How do common life events affect happiness.

Do people tend to adapt to common life events by converging back to a baseline level of happiness?

Clark et al. (2008) 12 use data from the German Socio-Economic Panel to identify groups of people experiencing significant life and labor market events and trace how these events affect the evolution of their life satisfaction. The visualization here shows an overview of their main findings. In each individual chart, the red lines mark the estimated effect of a different event at a given point in time (with ‘whiskers’ marking the range of confidence of each estimate).

In all cases, the results are split by gender, and time is labeled so that 0 marks the point when the corresponding event took place (with negative and positive values denoting years before and after the event). All estimates control for individual characteristics, so the figures show the effect of the event after controlling for other factors (e.g., income, etc.).

The first point to note is that most events denote the evolution of a latent situation: People grow unhappy in the period leading up to a divorce, while they grow happy in the period leading up to a marriage.

The second point is that single life events do tend to affect happiness in the short run, but people often adapt to changes. Of course, there are clear differences in the extent to which people adapt. In the case of divorce, life satisfaction first drops, then goes up and stays high. For unemployment, there is a negative shock both in the short and long run, notably among men. And for marriage, life satisfaction builds up before the wedding and fades out after it.

In general, the evidence suggests that adaptation is an important feature of well-being. Many common but important life events have a modest, long-term impact on self-reported happiness. Yet adaptation to some events, such as long-term unemployment, is neither perfect nor immediate.

Does disability correlate with life satisfaction?

A number of papers have noted that long-term paraplegics do not report themselves as particularly unhappy when compared to non-paraplegics (see, for example, the much-cited paper by Brickman, Coates, and Janoff-Bulman, 1978). 13

This assertion has received attention because it tells us something about the very meaning of well-being and has important consequences for policy. It is, for example, considered in courts of law with respect to compensation for disability.

However, comparing differences in self-reported life satisfaction among people with different disability statuses is not an ideal source of evidence regarding the effect of tragedy on happiness. Non-paraplegics are potentially different from paraplegics in ways that are hard to measure. Better sources of evidence are longitudinal surveys, where people are tracked over time.

Oswald and Powdthavee (2008) 14 use data from a longitudinal survey in the UK to explore whether accidents leading to disability imply long-term shocks to life satisfaction.

The chart here, from Oswald and Powdthavee, shows the average reported life satisfaction of a group of people who became seriously disabled (at time T) and remained seriously disabled in the two following years (T+1 and T+2). Here, ‘seriously disabled’ means that disability prevented them from being able to do day-to-day activities.

As we can see—and as the authors show more precisely through econometric techniques—those entering disability suffer a sudden drop in life satisfaction and recover only partially. This supports the idea that while adaptation plays a role in common life events, the notion of life satisfaction is indeed sensitive to tragic events.

Life satisfaction and society

Culture and life satisfaction.

Comparisons of happiness among countries suggest that culture and history shared by people in a given society matter for self-reported life satisfaction. For example, as the chart here shows, culturally and historically similar Latin American countries have a higher subjective well-being than other countries with comparable levels of economic development. (This chart plots self-reported life satisfaction as measured in the 10-point Cantril ladder on the vertical axis against GDP per capita on the horizontal axis).

Latin America is not a special case in this respect. Ex-communist countries, for example, tend to have lower subjective well-being than other countries with comparable characteristics and levels of economic development.

Academic studies in positive psychology discuss other patterns. Diener and Suh (2002) write: “In recent years cultural differences in subjective well-being have been explored, with a realization that there are profound differences in what makes people happy. Self-esteem, for example, is less strongly associated with life satisfaction, and extraversion is less strongly associated with pleasant affect in collectivist cultures than in individualist cultures”. 15

To our knowledge, there are no rigorous studies exploring the causal mechanisms linking culture and happiness. However, it seems natural to expect that cultural factors shape the way people collectively understand happiness and the meaning of life.

Sense of freedom and life satisfaction

A particular channel through which social environment may affect happiness is freedom: the society we live in may crucially affect the availability of options that we have to shape our own life.

This visualization shows the relationship between self-reported sense of freedom and self-reported life satisfaction using data from the Gallup World Poll . The variable measuring life satisfaction corresponds to country-level averages of survey responses to the Cantril Ladder question (a 0-10 scale, where 10 is the highest level of life satisfaction), while the variable measuring freedom corresponds to the share of people who agree with the statement “In this country, I am satisfied with my freedom to choose what I do with my life”. 16

As we can see, there is a clear positive relationship: countries, where people feel free to choose and control their lives, tend to be countries where people are happier. As Inglehart et al. (2008) 17 show this positive relationship holds even after we control for other factors, such as income and strength of religiosity.

Interestingly, this chart also shows that while there are some countries where the perceived sense of freedom is high but average life satisfaction is low (e.g., Rwanda), there are no countries where the perceived sense of freedom is low but average life satisfaction is high (i.e., there are no countries in the top left area of the chart).

To our knowledge, there are no rigorous studies exploring the causal mechanisms linking freedom and happiness. However, it seems natural to expect that self-determination and the absence of coercion are important components of what people consider a happy and meaningful life.

The link between media and gloominess

A number of studies have found that there is a link between emotional exposure to negative content in news and changes in mood.

Johnston and Davey (1997), 18 , for example, conducted an experiment in which they edited short TV news to display positive, neutral, or negative material and then showed them to three different groups of people. The authors found that people who watched the ‘negative’ clip were more likely to report a sad mood.

This link between emotional content in news and changes in mood is all the more important if we consider that media gatekeepers tend to prefer negative to positive coverage of newsworthy facts (see, for example, Combs and Slovic 1979 19 ).

Of course, mood is not the same as life satisfaction. Yet, as we discuss below in the section on measurement and data quality , surveys measuring happiness often do capture emotional aspects of well-being. In any case, people’s perceptions of what it means to lead a meaningful life are heavily influenced by their expectations of what is possible and likely to occur in their lives, and this has also been shown to depend on media exposure. 20

Data Quality and Measurement

Can ‘happiness’ really be measured.

The most natural way to attempt to measure subjective well-being is to ask people what they think and feel. Indeed, this is the most common approach.

In practice, social scientists tend to rely on questions inquiring directly about happiness or on questions inquiring about life satisfaction. The former tends to measure the experiential or emotional aspects of well-being (e.g., “I feel very happy”), while the latter tends to measure the evaluative or cognitive aspects of well-being (e.g., “I think I lead a very positive life”).

Self-reports about happiness and life satisfaction are known to correlate with things that people typically associate with contentment, such as cheerfulness and smiling. (In this scatter plot , you can see that countries where people have higher self-reported life satisfaction are also countries where people tend to smile more).

Experimental psychologists have also shown that self-reports of well-being from surveys turn out to correlate with activity in the parts of the brain associated with pleasure and satisfaction. Various surveys have confirmed that people who say they are happy also tend to sleep better and express positive emotions verbally more frequently.

Is ‘life satisfaction’ the same as ‘happiness’?

In this topic page, we discuss data and empirical research on happiness and life satisfaction. However, it is important to bear in mind that “life satisfaction” and “happiness” are not really synonyms. This is, of course, reflected in the data since self-reported measures of these two variables come from asking different kinds of questions.

The Integrated Values Surveys asks directly about happiness: “Taking all things together, would you say you are (i) Very happy, (ii) Rather happy, (iii) Not very happy, (iv) Not at all happy, (v) Don’t know.”

The Gallup World Poll, on the other hand, uses the Cantril Ladder question and asks respondents to evaluate their life: “Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you, and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?”

As the following scatter plot shows, these two measures tend to be related (countries that score high in one measure also tend to score high in the other), yet they are not identical (there is substantial dispersion, with many countries sharing the same score in one variable but diverging in the other).

The differences in responses to questions inquiring about life satisfaction and happiness are consistent with the idea that subjective well-being has two sides: an experiential or emotional side and an evaluative or cognitive side. Of course, the limits between emotional and cognitive aspects of well-being are blurred in our minds, so in practice, both kinds of questions measure both aspects to some degree. Indeed, social scientists often construct ‘subjective well-being indexes’ where they simply average out results from various types of questions.

Are happiness averages really meaningful?

The most common way to analyze data on happiness consists of taking averages across groups of people. Indeed, cross-country comparisons of self-reported life satisfaction, such as those presented in ‘happiness rankings’, rely on national averages of reports on a scale from 0 to 10 (the Cantril Ladder).

Is it reasonable to take averages of life satisfaction scores? Or, in more technical terms, are self-reports of Cantril scores really a cardinal measure of well-being?

The evidence tells us that survey-based reports on the Cantril Ladder do allow cardinal measurement reasonably well—respondents have been found to translate verbal labels, such as ‘very good’ and ‘very bad’, into roughly the same numerical values. 21 22

But as with any other aggregate indicator of social progress, averages need to be interpreted carefully, even if they make sense arithmetically. For example, if we look at happiness by age in a given country, we may see that older people do not appear to be happier than younger people. Yet this may be because the average-by-age figure from the snapshot confounds two factors: the age effect (people from the same cohort do get happier as they grow older, across all cohorts) and the cohort effect (across all ages, earlier generations are less happy than more recent generations). If the cohort effect is very strong, the snapshot can even give a picture that suggests people become less happy as they grow older, even though the exact opposite is actually true for all generations.

This example is, in fact, taken from the real world: using data from the US, Sutin et al. (2013) 23 showed that self-reported feelings of well-being tend to increase with age across generations, but overall levels of well-being depend on when people were born.

How much does language matter for cross-country comparisons of happiness?

Linguistic differences are often seen as a major obstacle for making cross-country comparisons of happiness. However, there is evidence suggesting that comparability issues, at least with respect to language, are less problematic than many people think.

Studies have shown, for example, that in interviews in which respondents are shown pictures or videos of other individuals, respondents can broadly identify whether the individual shown to them was happy or sad; this is also true when respondents were asked to predict the evaluations of individuals from other cultural communities. (For evidence of this, see Sandvik et al., 1993; Diener and Lucas, 1999). 24

Studies have also shown that ‘indigenous emotions’ across cultures (i.e., feelings that are unique in that they do not have equivalents in the English language) are not experienced any more frequently or differently than commonly translated emotions. (See Scollon et al. 2005). 25

The conclusion, therefore, seems to be that there is some basic understanding among humans about what it means to be ‘happy’. Survey-based measures of self-reported life satisfaction are informative about cross-country differences, even if these comparisons are obviously noisy.

A French translation of this topic page is available on our site: Bonheur et satisfaction .

Interactive charts on happiness and life satisfaction

Particularly important was the Stiglitz-Sen-Fitoussi Commission . It also relates to Bhutan’s famous measurement of Gross National Happiness (GNH) as an indicator of progress (Wikipedia here ).

To be precise, in 27 out of 31 countries with data spanning longer than one decade, the estimate for 2016 is higher than the earliest available estimate.

The GSS asks people a very similar question to the Integrated Values Survey: “Taken all together, how would you say things are these days—would you say that you are very happy, pretty happy, or not too happy?”

Stevenson, Betsey, and Justin Wolfers. “Happiness inequality in the United States.” The Journal of Legal Studies 37.S2 (2008): S33-S79. An ungated earlier version of the paper is available here .

These results have been discussed in various blogs. Freakonomics provides a quick and interesting overview of the debate, specifically with regard to gender gaps .

Clark, Andrew E., Sarah Flèche, and Claudia Senik. “Economic growth evens out happiness: Evidence from six surveys.” Review of Income and Wealth (2015). An ungated earlier version of the paper is available here

To be precise, the gradients correspond, country by country, to the regression coefficients between income quintiles and the related average life satisfaction reported by people within each income quintile.

Stevenson, B. and Wolfers, J. (2008). Economic growth and subjective well-being: Reassessing the Easterlin Paradox. Brookings Papers on Economic Activity, 1-87. An earlier version is available online here .

The dataset includes observations for Egypt. However, we have excluded these observations from our analysis. This is because the survey for Egypt in the wave labeled 2014 is from 2012, which was a year characterized by extreme political instability in that country.

R.A. Easterlin and L. Angelescu – ‘Modern Economic Growth and Quality of Life: Cross-Sectional and Time Series Evidence’ in Land, Michalos, and Sirgy (ed.) (2011) – Handbook of Social Indicators and Quality of Life Research. Springer.

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• Published: 19 June 2020

Well-being is more than happiness and life satisfaction: a multidimensional analysis of 21 countries

• Kai Ruggeri 1 , 2 ,
• Eduardo Garcia-Garzon 3 ,
• Áine Maguire 4 ,
• Sandra Matz 5 &
• Felicia A. Huppert 6 , 7  

Health and Quality of Life Outcomes volume  18 , Article number:  192 ( 2020 ) Cite this article

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Recent trends on measurement of well-being have elevated the scientific standards and rigor associated with approaches for national and international comparisons of well-being. One major theme in this has been the shift toward multidimensional approaches over reliance on traditional metrics such as single measures (e.g. happiness, life satisfaction) or economic proxies (e.g. GDP).

To produce a cohesive, multidimensional measure of well-being useful for providing meaningful insights for policy, we use data from 2006 and 2012 from the European Social Survey (ESS) to analyze well-being for 21 countries, involving approximately 40,000 individuals for each year. We refer collectively to the items used in the survey as multidimensional psychological well-being (MPWB).

The ten dimensions assessed are used to compute a single value standardized to the population, which supports broad assessment and comparison. It also increases the possibility of exploring individual dimensions of well-being useful for targeting interventions. Insights demonstrate what may be masked when limiting to single dimensions, which can create a failure to identify levers for policy interventions.

Conclusions

We conclude that both the composite score and individual dimensions from this approach constitute valuable levels of analyses for exploring appropriate policies to protect and improve well-being.

What is well-being?

Well-being has been defined as the combination of feeling good and functioning well; the experience of positive emotions such as happiness and contentment as well as the development of one’s potential, having some control over one’s life, having a sense of purpose, and experiencing positive relationships [ 23 ]. It is a sustainable condition that allows the individual or population to develop and thrive. The term subjective well-being is synonymous with positive mental health. The World Health Organization [ 45 ] defines positive mental health as “a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community”. This conceptualization of well-being goes beyond the absence of mental ill health, encompassing the perception that life is going well.

Well-being has been linked to success at professional, personal, and interpersonal levels, with those individuals high in well-being exhibiting greater productivity in the workplace, more effective learning, increased creativity, more prosocial behaviors, and positive relationships [ 10 , 27 , 37 ]. Further, longitudinal data indicates that well-being in childhood goes on to predict future well-being in adulthood [ 39 ]. Higher well-being is linked to a number of better outcomes regarding physical health and longevity [ 13 ] as well as better individual performance at work [ 30 ], and higher life satisfaction has been linked to better national economic performance [ 9 ].

Measurement of well-being

Governments and researchers have attempted to assess the well-being of populations for centuries [ 2 ]. Often in economic or political research, this has ended up being assessed using a single item about life satisfaction or happiness, or a limited set of items regarding quality of life [ 3 ]. Yet, well-being is a multidimensional construct, and cannot be adequately assessed in this manner [ 14 , 24 , 29 ]. Well-being goes beyond hedonism and the pursuit of happiness or pleasurable experience, and beyond a global evaluation (life satisfaction): it encompasses how well people are functioning, known as eudaimonic, or psychological well-being. Assessing well-being using a single subjective item approach fails to offer any insight into how people experience the aspects of their life that are fundamental to critical outcomes. An informative measure of well-being must encompass all the major components of well-being, both hedonic and eudaimonic aspects [ 2 ], and cannot be simplified to a unitary item of income, life satisfaction, or happiness.

Following acknowledgement that well-being measurement is inconsistent across studies, with myriad conceptual approaches applied [ 12 ], Huppert and So [ 27 ] attempted to take a systematic approach to comprehensively measure well-being. They proposed that positive mental health or well-being can be viewed as the complete opposite to mental ill health, and therefore attempted to define mental well-being in terms of the opposite of the symptoms of common mental disorders. Using the DSM-IV and ICD-10 symptom criteria for both anxiety and depression, ten features of psychological well-being were identified from defining the opposite of common symptoms. The features encompassed both hedonic and eudaimonic aspects of well-being: competence, emotional stability, engagement, meaning, optimism, positive emotion, positive relationships, resilience, self-esteem, and vitality. From these ten features an operational definition of flourishing, or high well-being, was developed using data from Round 3 of the European Social Survey (ESS), carried out in 2006. The items used in the Huppert and So [ 27 ] study were unique to that survey, which reflects a well-being framework based on 10 dimensions of good mental health. An extensive discussion on the development and validation of these measures for the framework is provided in this initial paper [ 27 ].

As this was part of a major, multinational social survey, each dimension was measured using a single item. As such, ‘multidimensional’ in this case refers to using available measures identified for well-being, but does not imply a fully robust measure of these individual dimensions, which would require substantially more items that may not be feasible for population-based work related to policy development. More detailed and nuanced approaches might help to better capture well-being as a multidimensional construct, and also may consider other dimensions. However, brief core measures such as the one implemented in the ESS are valuable as they provide a pragmatic way of generating pioneering empirical evidence on well-being across different populations, and help direct policies as well as the development of more nuanced instruments. While this naturally would benefit from complementary studies of robust measurement focused on a single topic, appropriate methods for using sprawling social surveys remain critical, particularly through better standardization [ 6 ]. While this paper will overview those findings, we strongly encourage more work to that end, particularly in more expansive measures to support policy considerations.

General approach and key questions

The aim of the present study was to develop a more robust measurement of well-being that allows researchers and policymakers to measure well-being both as a composite construct and at the level of its fundamental dimensions. Such a measure makes it possible to study overall well-being in a manner that goes beyond traditional single-item measures, which capture only a fraction of the dimensions of well-being, and because it allows analysts to unpack the measure into its core components to identify strengths and weaknesses. This would produce a similar approach as the most common reference for policy impacts: Gross Domestic Product (GDP), which is a composite measure of a large number of underlying dimensions.

The paper is structured as follows: in the first step, data from the ESS are used to develop a composite measure of well-being from the items suggested by Huppert & So [ 27 ] using factor analysis. In the second step, the value of the revised measure is demonstrated by generating insights into the well-being of 21 European countries, both at the level of overall well-being and at the level of individual dimensions.

The European social survey

The ESS is a biannual survey of European countries. Through comprehensive measurement and random sampling techniques, the ESS provides a representative sample of the European population for persons aged 15 and over [ 38 ]. Both Round 3 (2006–2007) and Round 6 (2012–2013) contained a supplementary well-being module. This module included over 50 items related to all aspects of well-being including psychological, social, and community well-being, as well as incorporating a brief measure of symptoms of psychological distress. As summarized by Huppert et al. [ 25 ], of the 50, only 30 items relate to personal well-being, of which only 22 are positive measures. Of those remaining, not all relate to the 10 constructs identified by Huppert and So [ 27 ], so only a single item could be used, or else the item that had the strongest face validity and distributional items were chosen.

Twenty-two countries participated in the well-being modules in both Round 3 and Round 6. As this it within a wider body of analyses, it was important to focus on those initially. Hungary did not have data for the vitality item in Round 3 and was excluded from the analysis, as appropriate models would not have been able to reliably resolve a missing item for an entire country. To be included in the analysis and remain consistent, participants therefore had to complete all 10 items used and have the age, gender, employment, and education variables completed. Employment was classified into four groups: students, employed, unemployed, retired; other groups were excluded. Education was classified into three groups: low (less than secondary school), middle (completed secondary school), and high (postsecondary study including any university and above). Using these criteria, the total sample for Round 6 was 41,825 people from 21 countries for analysis. The full sample was 52.6% female and ranged in age from 15 to 103 (M = 47.9; SD = 18.9). Other details about participation, response rates, and exclusion have been published elsewhere [ 38 ].

Huppert & So [ 27 ] defined well-being using 10 items extracted from the Round 3 items, which represent 10 dimensions of well-being. However, the items used in Round 3 to represent positive relationships and engagement exhibited ceiling effects and were removed from the questionnaire in Round 6. Four alternatives were available to replace each question. Based on their psychometric properties (i.e., absence of floor effects and wider response distributions), two new items were chosen for positive relationships and engagement (one item for each dimension). The new items and those they replaced can be seen in Table  1 (also see Supplement ).

Development of a composite measure of psychological well-being (MPWB)

A composite measure of well-being that yields an overall score for each individual was developed. From the ten indicators of well-being shown in Table 1 , a single factor score was calculated to represent MPWB. This overall MPWB score hence constitutes a summary of how an individual performs across the ten dimensions, which is akin to a summary score such as GDP, and will be of general value to policymakers. Statistical analysis was performed in R software, using lavaan [ 40 ] and lavaan.survey [ 35 ] packages. The former is a widely-used package for the R software designed for computing structural equation models and confirmatory factor analyses (CFA). The latter allows introducing complex survey design weights (combination of design and population size weights) when estimating confirmatory factor analysis models with lavaan, which ensures that MPWB scoring followed ESS guidelines regarding both country-level and survey specific weights [ 17 ]. Both packages have been previously tested and validated in various analyses using ESS data (as explained in detail in lavaan.survey documentation).

It should be noted that Round 6 was treated as the focal point of these efforts before repeating for Round 3, primarily due to the revised items that were problematic in Round 3, and considering that analyses of the 2006 data are already widely available.

Prior to analysis, all items were coded such that higher scores were more positive and lower scores more negative. Several confirmatory factor analysis models were performed in order to test several theoretical conceptualizations regarding MPWB. Finally, factor scores (expected a posteriori [ 15 ];) were calculated for the full European sample and used for descriptive purposes. The approach and final model are presented in supplemental material .

Factor scores are individual scores computed as weighted combinations of each person’s response on a given item and the factor scoring coefficients. This approach is to be preferred to using raw or sum scores: sum or raw scores fail to consider how well a given item serves as an indicator of the latent variable (i.e., all items are unrealistically assumed to be perfect and equivalent measures of MPWB). They also do not take into account that different items could present different variability, which is expected to occur if items present different scales (as in our case). Therefore, the use of such simple methods results in inaccurate individual rankings for MPWB. To resolve this, factor scores are both more informative and more accurate, as they avoid the propagation of measurement error in subsequent analyses [ 19 ].

Not without controversy (see Supplement ), factor scores are likely to be preferable to sum scores when ranking individuals on unobservable traits that are expected to be measured with noticeable measurement error (such as MPWB [ 32 ];). Similar approaches based on factor scoring have been successfully applied in large international assessment research [ 21 , 34 ]. With the aim of developing a composite well-being score, it was necessary to provide a meaningful representation of how the different well-being indicators are reflected in the single measure. A hierarchical model with one higher-order factor best approximated MPWB along with two first-order factors (see supplement Figure S 1 ). This model replicates the factor structure reported for Round 3 by Huppert & So [ 27 ]. The higher-order factor explained the relationship between two first-order factors (positive functioning and positive characteristics showed a correlation of ρ = .85). In addition, modelling standardized residuals showed that the items representing vitality and emotional stability and items representing optimism and self-esteem were highly correlated. The similarities in wording in both pairs of items (see Table 1 ) are suspected to be responsible for such high residual correlations. Thus, those correlations were included in the model. As presented in Table  2 , the hierarchical model was found to fit the data better than any other model but a bi-factor model including these correlated errors. The latter model resulted in collapsed factor structure with a weak, bi-polar positive functioning factor. However, this bi-factor model showed a problematic bi-polar group factor with weak loadings. Whether this group factor was removed (resulting in a S-1 bi-factor model, as in [ 16 ]), model fit deteriorated. Thus, neither bi-factor alternative was considered to be acceptable.

To calculate the single composite score representing MPWB, a factor scoring approach was used rather than a simplistic summing of raw scores on these items. Factor scores were computed and standardized for the sample population as a whole, which make them suitable for broad comparison [ 8 ]. This technique was selected for two reasons. First, it has the ability to take into account the different response scales used for measuring the items included in the multidimensional well-being model. The CFA model, from which MPWB scores were computed, was defined such that the metric of the MPWB was fixed, which results in a standardized scale. Alternative approaches, such as sum or raw scores, would result in ignoring the differential variability across items, and biased individual group scores. Our approach, using factor scoring, resolves this issue by means of standardization of the MPWB scores. The second reason for this technique is that it could take account of how strongly each item loaded onto the MPWB factor. It should be noted that by using only two sub-factors, the weight applied to the general factor is identical within the model for each round. This model was also checked to ensure it also was a good fit for different groups based on gender, age, education and employment.

Separate CFA analyses per each country indicate that the final model fit the data adequately in all countries (.971 < CFI < .995; .960 < TFI < .994; .020 < RMSEA < .05; 0,023 < SRMR < 0,042). All items presented substantive loadings on their respective factors, and structures consistently replicated across all tested countries. Largest variations were found when assessing the residual items’ correlations (e.g., for emotional stability and vitality correlation, values ranged from 0,076 to .394). However, for most cases, residuals correlations were of similar size and direction (for both cases, the standard deviation of estimated correlations was close of .10). Thus, strong evidence supporting our final model was systematically found across all analyzed countries. Full results are provided in the supplement (Tables S 2 -S 3 ).

Model invariance

In order to establish meaningful comparisons across groups within and between each country, a two-stage approach was followed, resulting in a structure that was successfully found to be similar across demographics. First, a descriptive comparison of the parameter estimates unveiled no major differences across groups. Second, factor scores were derived for the sample, employing univariate statistics to compare specific groups within country and round. In these analyses, neither traditional nor modern approaches to factor measurement invariance were appropriate given the large sample and number of comparisons at stake ([ 8 ]; further details in Supplement ).

From a descriptive standpoint, the hierarchical structure satisfactorily fit both Round 3 and Round 6 data. All indicators in both rounds had substantial factor loadings (i.e., λ > .35). A descriptive comparison of parameter estimates produced no major differences across the two rounds. The lack of meaningful differences in the parameter estimates confirms that this method for computing MPWB can be used in both rounds.

As MPWB scores from both rounds are obtained from different items that have different scales for responses, it is necessary to transform individual scores obtained from both rounds in order to be aligned. To do this between Round 3 and Round 6 items, a scaling approach was used. To produce common metrics, scores from Round 3 were rescaled using a mean and sigma transformation (Kolen & Brennan 2010) to align with Round 6 scales. This was used as Round 6 measures were deemed to have corrected some deficiencies found in Round 3 items. This does not change outcomes in either round but simply makes the scores match in terms of distributions relative to their scales, making them more suitable for comparison.

As extensive descriptive insights on the sample and general findings are already available (see [ 41 ]), we focus this section on the evidence derived directly from the proposed approach to MPWB scores. For the combined single score for MPWB, the overall mean (for all participants combined) is fixed to zero, and the scores represent deviation from the overall mean. In 2012 (Round 6), country scores on well-being ranged from − 0.41 in Bulgaria to 0.46 in Denmark (Fig.  1 ). There was a significant, positive relationship between national MPWB mean scores and national life satisfaction means ( r =  .56 (.55–.57), p  < .001). In addition, MPWB was negatively related with depression scores and positively associated with other well-being measurements (see Supplement ).

Distribution of national MPWB means and confidence intervals across Europe

Denmark having the highest well-being is consistent with many studies [ 4 , 18 ] and with previous work using ESS data [ 27 ]. While the pattern is typically that Nordic countries are doing the best and that eastern countries have the lowest well-being, exceptions exist. The most notable exception is Portugal, which has the third-lowest score and is not significantly higher than Ukraine, which is second lowest. Switzerland and Germany are second and third highest respectively, and show generally similar patterns to the Scandinavian countries (see Fig. 1 ). It should be noted that, for Figs.  1 , 2 , 3 , 4 , 5 , countries with the lowest well-being are at the top. This is done to highlight the greatest areas for potential impact, which are also the most of concern to policy.

Well-being by country and gender

Well-being by country and age

Well-being by country and employment

Well-being by country and education

General patterns across the key demographic variables – gender, age, education, employment – are visible across countries as seen in Figs.  1 , 2 , 3 , 4 , 5 (see also Supplement 2 ). These figures highlight patterns based on overall well-being as well as potential for inequalities. The visualizations presented here, though univariate, are for the purpose of understanding broad patterns while highlighting the need to disentangle groups and specific dimensions to generate effective policies.

For gender, women exhibited lower MPWB scores than men across Europe (β = −.09, t (36508) = − 10.37; p  < .001). However, these results must be interpreted with caution due to considerable overlap in confidence intervals for many of the countries, and greater exploration of related variables is required. This also applies for the five countries (Estonia, Finland, Ireland, Slovakia, Ukraine) where women have higher means than men. Only four countries have significant differences between genders, all of which involve men having higher scores than women: the Netherlands (β = −.12, t (1759) = − 3.24; p  < .001), Belgium (β = −.14, t (1783) = − 3.94; p  < .001), Cyprus (β = −.18, t (930) = − 2.87; p  < .001) and Portugal (β = −.19, t (1847) = − 2.50; p  < .001).

While older individuals typically exhibited lower MPWB scores compared to younger age groups across Europe (β 25–44  = −.05, t (36506) = − 3.686, p  < .001; β 45–65  = −.12, t (36506) = − 8.356, p  < .001; β 65–74  = −.16, t (36506) = − 8.807, p  < .001; β 75+  = −.28, t (36506) = − 13.568, p  < .001), the more compelling pattern shows more extreme differences within and between age groups for the six countries with the lowest well-being. This pattern is most pronounced in Bulgaria, which has the lowest overall well-being. For the three countries with the highest well-being (Denmark, Switzerland, Germany), even the mean of the oldest age group was well above the European average, while for the countries with the lowest well-being, it was only young people, particularly those under 25, who scored above the European average. With the exception of France and Denmark, countries with higher well-being typically had fewer age group differences and less variance within or between groups. Only countries with the lowest well-being showed age differences that were significant with those 75 and over showing the lowest well-being.

MPWB is consistently higher for employed individuals and students than for retired (β = −.31, t (36506) = − 21.785; p  < .00) or unemployed individuals (β = −.52, t (36556) = − 28.972; p  < .001). Unemployed groups were lowest in nearly all of the 21 countries, though the size of the distance from other groups did not consistently correlate with national MPWB mean. Unemployed individuals in the six countries with the lowest well-being were significantly below the mean, though there is little consistency across groups and countries by employment beyond that. In countries with high well-being, unemployed, and, in some cases, retired individuals, had means below the European average. In countries with the lowest well-being, it was almost exclusively students who scored above the European average. Means for retired groups appear to correlate most strongly with overall well-being. There is minimal variability for employed groups in MPWB means within and between countries.

There is a clear pattern of MPWB scores increasing with education level, though the differences were most pronounced between low and middle education groups (β = .12, t (36508) = 9.538; p  < .001). Individuals with high education were significantly higher on MPWB than those in the middle education group (β = .10, t (36508) =11.06; p  < .001). Differences between groups were noticeably larger for countries with lower overall well-being, and the difference was particularly striking in Bulgaria. In Portugal, medium and high education well-being means were above the European average (though 95% confidence intervals crossed 0), but educational attainment is significantly lower in the country, meaning the low education group represents a greater proportion of the population than the other 21 countries. In the six countries with the highest well-being, mean scores for all levels of education were above the European mean.

Utilizing ten dimensions for superior understanding of well-being

It is common to find rankings of national happiness and well-being in popular literature. Similarly, life satisfaction is routinely the only measure reported in many policy documents related to population well-being. To demonstrate why such limited descriptive approaches can be problematic, and better understood using multiple dimensions, all 21 countries were ranked individually on each of the 10 indicators of well-being and MPWB in Round 6 based on their means. Figure  6 demonstrates the variations in ranking across the 10 dimensions of well-being for each country.

Country rankings in 2012 on multidimensional psychological well-being and each of its 10 dimensions

The general pattern shows typically higher rankings for well-being dimensions in countries with higher overall well-being (and vice-versa). Yet countries can have very similar scores on the composite measure but very different underlying profiles in terms of individual dimensions. Figure  7 a presents this for two countries with similar life satisfaction and composite well-being, Belgium and the United Kingdom. Figure 7 b then demonstrates this even more vividly for two countries, Finland and Norway, which have similar composite well-being scores and identical mean life satisfaction scores (8.1), as well as have the highest two values for happiness of all 21 countries. In both pairings, the broad outcomes are similar, yet countries consistently have very different underlying profiles in individual dimensions. The results indicate that while overall scores can be useful for general assessment, specific dimensions may vary substantially, which is a relevant first step for developing interventions. Whereas the ten items are individual measures of 10 areas of well-being, had these been limited to a single domain only, the richness of the underlying patterns would have been lost, and the limitation of single item approaches amplified.

a Comparison of ranks for dimensions of well-being between two different countries with similar life satisfaction in 2012: Belgium and United Kingdom. b Comparison of ranks for dimensions of well-being between two similar countries with identical life satisfaction and composite well-being scores in 2012: Finland and Norway

The ten-item multidimensional measure provided clear patterns for well-being across 21 countries and various groups within. Whether used individually or combined into a composite score, this approach produces more insight into well-being and its components than a single item measure such as happiness or life satisfaction. Fundamentally, single items are impossible to unpack in reverse to gain insights, whereas the composite score can be used as a macro-indicator for more efficient overviews as well as deconstructed to look for strengths and weaknesses within a population, as depicted in Figs.  6 and 7 . Such deconstruction makes it possible to more appropriately target interventions. This brings measurement of well-being in policy contexts in line with approaches like GDP or national ageing indexes [ 7 ], which are composite indicators of many critical dimensions. The comparison with GDP is discussed at length in the following sections.

Patterns within and between populations

Overall, the patterns and profiles presented indicate a number of general and more nuanced insights. The most consistent among these is that the general trend in national well-being is usually matched within each of the primary indicators assessed, such as lower well-being within unemployed groups in countries with lower overall scores than in those with higher overall scores. While there are certainly exceptions, this general pattern is visible across most indicators.

The other general trend is that groups with lower MPWB scores consistently demonstrate greater variability and wider confidence intervals than groups with higher scores. This is a particularly relevant message for policymakers given that it is an indication of the complexity of inequalities: improvements for those doing well may be more similar in nature than for those doing poorly. This is particularly true for employment versus unemployment, yet reversed for educational attainment. Within each dimension, the most critical pattern is the lack of consistency for how each country ranks, as discussed further in other sections.

Examining individual dimensions of well-being makes it possible to develop a more nuanced understanding of how well-being is impacted by societal indicators, such as inequality or education. For example, it is possible that spending more money on education improves well-being on some dimensions but not others. Such an understanding is crucial for the implementation of targeted policy interventions that aim at weaker dimensions of well-being and may help avoid the development of ineffective policy programs. It is also important to note that the patterns across sociodemographic variables may differ when all groups are combined, compared to results within countries. Some effects may be larger when all are combined, whereas others may have cancelling effects.

Using these insights, one group that may be particularly important to consider is unemployed adults, who consistently have lower well-being than employed individuals. Previous research on unemployment and well-being has often focused on mental health problems among the unemployed [ 46 ] but there are also numerous studies of differences in positive aspects of well-being, mainly life satisfaction and happiness [ 22 ]. A large population-based study has demonstrated that unemployment is more strongly associated with the absence of positive well-being than with the presence of symptoms of psychological distress [ 28 ], suggesting that programs that aim to increase well-being among unemployed people may be more effective than programs that seek to reduce psychological distress.

Certainly, it is well known that higher income is related to higher subjective well-being and better health and life expectancy [ 1 , 42 ], so reduced income following unemployment is likely to lead to increased inequalities. Further work would be particularly insightful if it included links to specific dimensions of well-being, not only the comprehensive scores or overall life satisfaction for unemployed populations. As such, effective responses would involve implementation of interventions known to increase well-being in these groups in times of (or in spite of) low access to work, targeting dimensions most responsible for low overall well-being. Further work on this subject will be presented in forthcoming papers with extended use of these data.

This thinking also applies to older and retired populations in highly deprived regions where access to social services and pensions are limited. A key example of this is the absence in our data of a U-shaped curve for age, which is commonly found in studies using life satisfaction or happiness [ 5 ]. In our results, older individuals are typically lower than what would be expected in a U distribution, and in some cases, the oldest populations have the lowest MPWB scores. While previous studies have shown some decline in well-being beyond the age of 75 [ 20 ], our analysis demonstrates quite a severe fall in MPWB in most countries. What makes this insight useful – as opposed to merely unexpected – is the inclusion of the individual dimensions such as vitality and positive relationships. These dimensions are clearly much more likely to elicit lower scores than for younger age groups. For example, ageing beyond 75 is often associated with increased loneliness and isolation [ 33 , 43 ], and reduction in safe, independent mobility [ 31 ], which may therefore correspond with lower scores on positive relationships, engagement, and vitality, and ultimately lower scores on MPWB than younger populations. Unpacking the dimensions associated with the age-related decline in well-being should be the subject of future research. The moderate positive relationship of MPWB scores with life satisfaction is clear but also not absolute, indicating greater insights through multidimensional approaches without any obvious loss of information. Based on the findings presented here, it is clearly important to consider ensuring the well-being of such groups, the most vulnerable in society, during periods of major social spending limitations.

Policy implications

Critically, Fig.  6 represents the diversity of how countries reach an overall MPWB score. While countries with overall high well-being have typically higher ranks on individual items, there are clearly weak dimensions for individual countries. Conversely, even countries with overall low well-being have positive scores on some dimensions. As such, the lower items can be seen as potential policy levers in terms of targeting areas of concern through evidence-based interventions that should improve them. Similarly, stronger areas can be seen as learning opportunities to understand what may be driving results, and thus used to both sustain those levels as well as potentially to translate for individuals or groups not performing as well in that dimension. Collectively, we can view this insight as a message about specific areas to target for improvement, even in countries doing well, and that even countries doing poorly may offer strengths that can be enhanced or maintained, and could be further studied for potential applications to address deficits. We sound a note of caution however, in that these patterns are based on ranks rather than actual values, and that those ranks are based on single measures.

Figure 7 complements those insights more specifically by showing how Finland and Norway, with a number of social, demographic, and economic similarities, plus identical life satisfaction scores (8.1) arrive at similar single MPWB scores with very different profiles for individual dimensions. By understanding the levers that are specific to each country (i.e. dimensions with the lowest well-being scores), policymakers can respond with appropriate interventions, thereby maximizing the potential for impact on entire populations. Had we restricted well-being measurement to a single question about happiness, as is commonly done, we would have seen both countries had similar and extremely high means for happiness. This might have led to the conclusion that there was minimal need for interventions for improving well-being. Thus, in isolation, using happiness as the single indicator would have masked the considerable variability on several other dimensions, especially those dimensions where one or both had means among the lowest of the 21 countries. This would have resulted in similar policy recommendations, when in fact, Norway may have been best served by, for example, targeting lower dimensions such as Engagement and Self-Esteem, and Finland best served by targeting Vitality and Emotional Stability.

Targeting specific groups and relevant dimensions as opposed to comparing overall national outcomes between countries is perhaps best exemplified by Portugal, which has one of the lowest educational attainment rates in OECD countries, exceeded only by Mexico and Turkey [ 36 ]. This group thus skews the national MPWB score, which is above average for middle and high education groups, but much lower for those with low education. Though this pattern is not atypical for the 21 countries presented here, the size of the low education group proportional to Portugal’s population clearly reduces the national MPWB score. This implies that the greatest potential for improvement is likely to be through addressing the well-being of those with low education as a near-term strategy, and improving access to education as a longer-term strategy. It will be important to analyze this in the near future, given recent reports that educational attainment in Portugal has increased considerably in recent years (though remains one of the lowest in OECD countries) [ 36 ].

One topic that could not be addressed directly is whether these measures offer value as indicators of well-being beyond the 21 countries included here, or even beyond the countries included in ESS generally. In other words, are these measures relevant only to a European population or is our approach to well-being measurement translatable to other regions and purposes? Broadly speaking, the development of these measures being based on DSM and ICD criteria should make them relevant beyond just the 21 countries, as those systems are generally intended to be global. However, it can certainly be argued that these methods for designing measures are heavily influenced by North American and European medical frameworks, which may limit their appropriateness if applied in other regions. Further research on these measures should consider this by adding potential further measures deemed culturally appropriate and seeing if comparable models appear as a result.

A single well-being score

One potential weakness remains the inconsistency of scaling between ESS well-being items used for calculating MPWB. However, this also presents an opportunity to consider the relative weighting of each item within the current scales, and allow for the development of a more consistent and reliable measure. These scales could be modified to align in separate studies with new weights generated – either generically for all populations or stratified to account for various cultural or other influences. Using these insights, scales could alternatively be produced to allow for simple scoring for a more universally accessible structure (e.g. 1–100) but with appropriate values for each item that represents the dimensions, if this results in more effective communication with a general public than a standardized score with weights. Additionally, common scales would improve on attempts to use rankings for presenting national variability within and between dimensions. Researchers should be aware that factor scores are sample-dependent (as based on specific factor model parameters such as factor loadings). Nevertheless, future research focused on investigating specific item differential functioning (by means of multidimensional item response functioning or akin techniques) of these items across situations (i.e., rounds) and samples (i.e., rounds and countries) should be conducted in order to have a more nuanced understanding of this scale functioning.

What makes this discussion highly relevant is the value of a more informed measure to replace traditional indicators of well-being, predominantly life satisfaction. While life satisfaction may have an extensive history and present a useful metric for comparisons between major populations of interest, it is at best a corollary, or natural consequence, of other indicators. It is not in itself useful for informing interventions, in the same way limiting to a single item for any specific dimension of well-being should not alone inform interventions.

By contrast, a validated and standardized multidimensional measure is exceptionally useful in its suitability to identify those at risk, as well as its potential for identifying areas of strengths and weaknesses within the at-risk population. This can considerably improve the efficiency and appropriateness of interventions. It identifies well-understood dimensions (e.g. vitality, positive emotion) for direct application of evidence-based approaches that would improve areas of concern and thus overall well-being. Given these points, we strongly argue for the use of multidimensional approaches to measurement of well-being for setting local and national policy agenda.

There are other existing single-score approaches for well-being addressing its multidimensional nature. These include the Warwick-Edinburgh Mental Well-Being Scale [ 44 ] and the Flourishing Scale [ 11 ]. In these measures, although the single score is derived from items that clearly tap a number of dimensions, the dimensions have not been systematically derived and no attempt is made to measure the underlying dimensions individually. In contrast, the development approach used here – taking established dimensions from DSM and ICD – is based on years of international expertise in the field of mental illness. In other words, there have long been adequate measures for identifying and understanding illness, but there is room for improvement to better identify and understand health. With increasing support for the idea of these being a more central focus of primary outcomes within economic policies, such approaches are exceptionally useful [ 13 ].

Better measures, better insights

Naturally, it is not a compelling argument to simply state that more measures present greater information than fewer or single measures, and this is not the primary argument of this manuscript. In many instances, national measures of well-being are mandated to be restricted to a limited set of items. What is instead being argued is that well-being itself is a multidimensional construct, and if it is deemed a critical insight for establishing policy agenda or evaluating outcomes, measurements must follow suit and not treat happiness and life satisfaction values as universally indicative. The items included in ESS present a very useful step to that end, even in a context where the number of items is limited.

As has been argued by many, greater consistency in measurement of well-being is also needed [ 26 ]. This may come in the form of more consistency regarding dimensions included, the way items are scored, the number of items representing each dimension, and changes in items over time. While inconsistency may be prevalent in the literature to date for definitions and measurement, the significant number of converging findings indicates increasingly robust insights for well-being relevant to scientists and policymakers. Improvements to this end would support more systematic study of (and interventions for) population well-being, even in cases where data collection may be limited to a small number of items.

The added value of MPWB as a composite measure

While there are many published arguments (which we echo) that measures of well-being must go beyond objective features, particularly related to economic indicators such as GDP, this is not to say one replaces the other. More practically, subjective and objective approaches will covary to some degree but remain largely distinct. For example, GDP presents a useful composite of a substantial number of dimensions, such as consumption, imports, exports, specific market outcomes, and incomes. If measurement is restricted to a macro-level indicator such as GDP, we cannot be confident in selecting appropriate policies to implement. Policies are most effective when they target a specific component (of GDP, in this instance), and then are directly evaluated in terms of changes in that component. The composite can then be useful for comprehensive understanding of change over time and variation in circumstances. Specific dimensions are necessary for identifying strengths and weaknesses to guide policy, and examining direct impacts on those dimensions. In this way, a composite measure in the form of MPWB for aggregate well-being is also useful, so long as the individual dimensions are used in the development and evaluation of policies. Similar arguments for other multidimensional constructs have been made recently, such as national indexes of ageing [ 7 ].

In the specific instance of MPWB in relation to existing measures of well-being, there are several critical reasons to ensure a robust approach to measurement through systematic validation of psychometric properties. The first is that these measures are already part of the ESS, meaning they are being used to study a very large sample across a number of social challenges and not specifically a new measure for well-being. The ESS has a significant influence on policy discussions, which means the best approaches to utilizing the data are critical to present systematically, as we have attempted to do here. This approach goes beyond existing measures such as Gallup or the World Happiness Index to broadly cover psychological well-being, not individual features such as happiness or life satisfaction (though we reiterate: as we demonstrate in Fig.  7 a and b, these individual measures can and should still covary broadly with any multidimensional measure of well-being, even if not useful for predicting all dimensions). While often referred to as ‘comprehensive’ measurement, this merely describes a broad range of dimensions, though more items for each dimension – and potentially more dimensions – would certainly be preferable in an ideal scenario.

These dimensions were identified following extensive study for flourishing measures by Huppert & So [ 27 ], meaning they are not simply a mix of dimensions, but established systematically as the key features of well-being (the opposite of ill-being). Furthermore, the development of the items is in line with widely validated and practiced measures for the identification of illness. The primary adjustment has simply been the emphasis on health, but otherwise maintains the same principles of assessment. Therefore, the overall approach offers greater value than assessing only negative features and inferring absence equates to opposite (positives), or that individual measures such as happiness can sufficiently represent a multidimensional construct like well-being. Collectively, we feel the approach presented in this work is therefore a preferable method for assessing well-being, particularly on a population level, and similar approaches should replace single items used in isolation.

While the focus of this paper is on the utilization of a widely tested measure (in terms of geographic spread) that provides for assessing population well-being, it is important to provide a specific application for why this is relevant in a policy context. Additionally, because the ESS itself is a widely-recognized source of meaningful information for policymakers, providing a robust and comprehensive exploration of the data is necessary. As the well-being module was not collected in recent rounds, these insights provide clear reasoning and applications for bringing them back in the near future.

More specifically, it is critical that this approach be seen as advantageous both in using the composite measure for identifying major patterns within and between populations, and for systematically unpacking individual dimensions. Using those dimensions produces nuanced insights as well as the possibility of illuminating policy priorities for intervention.

In line with this, we argue that no composite measure can be useful for developing, implementing, or evaluating policy if individual dimensions are not disaggregated. We are not arguing that MPWB as a single composite score, nor the additional measures used in ESS, is better than other existing single composite scoring measures of well-being. Our primary argument is instead that MPWB is constructed and analyzed specifically for the purpose of having a robust measure suitable for disaggregating critical dimensions of well-being. Without such disaggregation, single composite measures are of limited use. In other words, construct a composite and target the components.

Well-being is perhaps the most critical outcome measure of policies. Each individual dimension of well-being as measured in this study represents a component linked to important areas of life, such as physical health, financial choice, and academic performance [ 26 ]. For such significant datasets as the European Social Survey, the use of the single score based on the ten dimensions included in multidimensional psychological well-being gives the ability to present national patterns and major demographic categories as well as to explore specific dimensions within specific groups. This offers a robust approach for policy purposes, on both macro and micro levels. This facilitates the implementation and evaluation of interventions aimed at directly improving outcomes in terms of population well-being.

Availability of data and materials

The datasets analysed during the current study are available in the European Social Survey repository, http://www.europeansocialsurvey.org/data/country_index.html

Abbreviations

Diagnostic and Statistical Manual of Mental Disorders

European Social Survey

Gross Domestic Product

International Classification of Disease

Multidimensional psychological well-being

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Acknowledgements

The authors would like to thank Ms. Sara Plakolm, Ms. Amel Benzerga, and Ms. Jill Hurson for assistance in proofing the final draft. We would also like to acknowledge the general involvement of the Centre for Comparative Social Surveys at City University, London, and the Centre for Wellbeing at the New Economics Foundation.

This work was supported by a grant from the UK Economic and Social Research Council (ES/LO14629/1). Additional support was also provided by the Isaac Newton Trust, Trinity College, University of Cambridge, and the UK Economic and Social Research Council (ES/P010962/1).

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KR is the lead author and researcher on the study, responsible for all materials start to finish. FH was responsible for the original grant award and the general theory involved in the measurement approaches. ÁM was responsible for broad analysis and writing. EGG was responsible for psychometric models and the original factor scoring approach, plus writing the supplementary explanations. SM provided input on later drafts of the manuscript as well as the auxiliary analyses. The authors read and approved the final manuscript.

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. Hierarchical approach to modelling comprehensive psychological well-being. Table S1 . Confirmatory Factor Structure for Round 6 and 3. Figure S2 . Well-being by country and gender. Figure S3 . Well-being by country and age. Figure S4 . Well-being by country and employment. Figure S5 . Well-being by country and education. Table S2 . Item loadings for Belgium to Great Britain. Table S3 . Item loadings for Ireland to Ukraine.

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Ruggeri, K., Garcia-Garzon, E., Maguire, Á. et al. Well-being is more than happiness and life satisfaction: a multidimensional analysis of 21 countries. Health Qual Life Outcomes 18 , 192 (2020). https://doi.org/10.1186/s12955-020-01423-y

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• Published: 20 July 2023

A systematic review of the strength of evidence for the most commonly recommended happiness strategies in mainstream media

• Dunigan Folk   ORCID: orcid.org/0000-0001-6728-2776 1 &
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We conducted a systematic review of the evidence underlying some of the most widely recommended strategies for increasing happiness. By coding media articles on happiness, we first identified the five most commonly recommended strategies: expressing gratitude, enhancing sociability, exercising, practising mindfulness/meditation and increasing nature exposure. Next, we conducted a systematic search of the published scientific literature. We identified well-powered, pre-registered experiments testing the effects of these strategies on any aspect of subjective wellbeing (that is, positive affect, negative affect and life satisfaction) in non-clinical samples. A total of 57 studies were included. Our review suggests that a strong scientific foundation is lacking for some of the most commonly recommended happiness strategies. As the effectiveness of these strategies remains an open question, there is an urgent need for well-powered, pre-registered studies investigating strategies for promoting happiness.

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The results of the media search are available on the Open Science Framework at https://tinyurl.com/2kkayzh9 .

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Acknowledgements

We thank S. Lyubomirsky and H. Passmore for comments on a previous version of this manuscript, and M. Smith for providing guidance on conducting the systematic literature search. We also thank J. Tan, P. Ramachadran, R. Li, R. Kaur, C. Peretz and C. Cardle for assistance with the media and scholarly literature searches. Our work was supported by grant #GR012572 from the Social Sciences and Humanities Research Council of Canada (SSHRC; E.D.) and the SSHRC Doctoral Award #6567 (D.F.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Folk, D., Dunn, E. A systematic review of the strength of evidence for the most commonly recommended happiness strategies in mainstream media. Nat Hum Behav 7 , 1697–1707 (2023). https://doi.org/10.1038/s41562-023-01651-4

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World Happiness Report 2024: most comprehensive picture yet of happiness across generations

Related news.

Fresh insights from the World Happiness Report 2024, released today, paint the richest picture yet of happiness trends across different ages and generations.

The report is a partnership of Gallup , the Oxford Wellbeing Research Centre , the UN Sustainable Development Solutions Network , and the WHR’s Editorial Board. Jan-Emmanuel De Neve ,  Professor of Economics and Behavioural Science at Saïd Business School and Director of the Wellbeing Research Centre, is an editor of the report. 

The 2024 findings , announced today to mark the UN’s International Day of Happiness , are powered by data from the Gallup World Poll and analysed by some of the world’s leading wellbeing scientists. 

Experts used responses from people in more than 140 nations to rank the world’s ‘happiest’ countries. Finland tops the overall list for the seventh successive year, though there is considerable movement elsewhere:

• Serbia (37th) and Bulgaria (81st) have had the biggest increases in average life evaluation scores since they were first measured by the Gallup World Poll in 2013, and this is reflected in climbs up the rankings between World Happiness Report 2013 and this 2024 edition of 69 places for Serbia and 63 places for Bulgaria.
• The next two countries showing the largest increases in life evaluations are Latvia (46th) and Congo (Brazzaville) (89th), with rank increases of 44 and 40 places, respectively, between 2013 and 2024.

It is a great privilege and responsibility for our Centre at Oxford to become the next custodian of the World Happiness Report. We’re committed to continuing to give the best evidence on the state of global happiness together with our partners. Jan-Emmanuel de Neve An editor of the World Happiness Report and Oxford Saïd Professor

Significantly, the United States of America (23rd) has fallen out of the top 20 for the first time since the World Happiness Report was first published in 2012, driven by a large drop in the wellbeing of Americans under 30. Afghanistan remains bottom of the overall rankings as the world’s ‘unhappiest’ nation.

For the first time, the report gives separate rankings by age group, in many cases varying widely from the overall rankings. Lithuania tops the list for children and young people under 30, while Denmark is the world’s happiest nation for those 60 and older.

In comparing generations, those born before 1965 are, on average, happier than those born since 1980. Among Millennials, evaluation of one’s own life drops with each year of age, while among Boomers life satisfaction increases with age.

Rankings are based on a three-year average of each population’s average assessment of their quality of life. Interdisciplinary experts from the fields of economics, psychology, sociology and beyond then attempt to explain the variations across countries and over time using factors such as GDP, life expectancy, having someone to count on, a sense of freedom, generosity and perceptions of corruption.

These factors help to explain the differences across nations, while the rankings themselves are based only on the answers people give when asked to rate their own lives.

Jan-Emmanuel said: 'Once again, the World Happiness Report uncovers some special empirical insights at the cutting edge of the wellbeing research frontier. Piecing together the available data on the wellbeing of children and adolescents around the world, we documented disconcerting drops especially in North America and Western Europe. To think that, in some parts of the world, children are already experiencing the equivalent of a mid-life crisis demands immediate policy action.

'It is a great privilege and responsibility for our Centre at Oxford to become the next custodian of the World Happiness Report and we’re committed to continuing to give the world the best evidence on the state of global happiness in collaboration with our partners.'

Last year the Vice-Chancellor of Oxford University, Irene Tracey, invited Jan-Emmanuel onto her podcast series - Fire & Wire - where they discussed the business case for increasing wellbeing in a workplace context, and the impacts improved wellbeing can have on organisations. 

The World Happiness Report 2024 also features curated submissions on the theme of happiness across different age groups from experts at the forefront of wellbeing science.

Observing the state of happiness among the world’s children and adolescent population, researchers found that, globally, young people aged 15 to 24 report higher life satisfaction than older adults, but this gap is narrowing in Europe and recently reversed in North America.

Findings also suggest that the wellbeing of 15- to 24-year-olds has fallen in North America, Western Europe, the Middle East and North Africa, and South Asia since 2019 – but in the rest of world it has risen. Overall, though, there is a notable global scarcity of wellbeing data available for children below the age of 15.

Further work examines the relationship between wellbeing and dementia, identified as a significant area of research in a globally aging population.

Researchers highlight not only the impact of dementia on the wellbeing of individuals but also the reverse association: the demonstrable predictive power of higher wellbeing to reduce the risk of developing the disease in later life.

Finally, a team of researchers used a large survey of life satisfaction of older adults in what is now the world’s most populous nation: India. They found that within this older Indian population, increasing age is associated with higher life satisfaction, matching the findings of the global analyses.

These researchers also analysed the complex impact of India’s caste system on wellbeing among older adults, though satisfaction with living arrangements, perceived discrimination and self-rated health emerged as the top three predictors of life satisfaction in this study.

The World Happiness Report is a partnership of Gallup, the Oxford Wellbeing Research Centre, the UN Sustainable Development Solutions Network, and the WHR’s Editorial Board. 

ORIGINAL RESEARCH article

The art of happiness: an explorative study of a contemplative program for subjective well-being.

• 1 Department of Psychology and Cognitive Science, University of Trento, Trento, Italy
• 2 Department of Psychology, Sapienza University of Rome, Rome, Italy
• 3 Institute Lama Tzong Khapa, Pisa, Italy

In recent decades, psychological research on the effects of mindfulness-based interventions has greatly developed and demonstrated a range of beneficial outcomes in a variety of populations and contexts. Yet, the question of how to foster subjective well-being and happiness remains open. Here, we assessed the effectiveness of an integrated mental training program The Art of Happiness on psychological well-being in a general population. The mental training program was designed to help practitioners develop new ways to nurture their own happiness. This was achieved by seven modules aimed at cultivating positive cognition strategies and behaviors using both formal (i.e., lectures, meditations) and informal practices (i.e., open discussions). The program was conducted over a period of 9 months, also comprising two retreats, one in the middle and one at the end of the course. By using a set of established psychometric tools, we assessed the effects of such a mental training program on several psychological well-being dimensions, taking into account both the longitudinal effects of the course and the short-term effects arising from the intensive retreat experiences. The results showed that several psychological well-being measures gradually increased within participants from the beginning to the end of the course. This was especially true for life satisfaction, self-awareness, and emotional regulation, highlighting both short-term and longitudinal effects of the program. In conclusion, these findings suggest the potential of the mental training program, such as The Art of Happiness , for psychological well-being.

Introduction

People desire many valuable things in their life, but—more than anything else—they want happiness ( Diener, 2000 ). The sense of happiness has been conceptualized as people's experienced well-being in both thoughts and feelings ( Diener, 2000 ; Kahneman and Krueger, 2006 ). Indeed, research on well-being suggests that the resources valued by society, such as mental health ( Koivumaa-Honkanen et al., 2004 ) and a long life ( Danner et al., 2001 ), associate with high happiness levels. Since the earliest studies, subjective well-being has been defined as the way in which individuals experience the quality of their life in three different but interrelated mental aspects: infrequent negative affect, frequent positive affect, and cognitive evaluations of life satisfaction in various domains (physical health, relationships, and work) ( Diener, 1984 , 1994 , 2000 ; Argyle et al., 1999 ; Diener et al., 1999 ; Lyubomksky et al., 2005 ; Pressman and Cohen, 2005 ). A growing body of research has been carried out aimed at identifying the factors that affect happiness, operationalized as subjective well-being. In particular, the construct of happiness is mainly studied within the research fields of positive psychology or contemplative practices, which are grounded in ancient wisdom traditions. Positive psychology has been defined as the “the scientific study of human strengths and virtues” ( Sheldon and King, 2001 ), and it can be traced back to the reflections of Aristotle about different perspectives on well-being ( Ryan and Deci, 2001 ). On the other end, contemplative practices include a great variety of mental exercises, such as mindfulness, which has been conceived as a form of awareness that emerges from experiencing the present moment without judging those experiences ( Kabat-Zinn, 2003 ; Bishop et al., 2004 ). Most of these exercises stem from different Buddhist contemplative traditions such as Vipassana and Mahayana ( Kornfield, 2012 ). Notably, both perspective share the idea of overcoming suffering and achieving happiness ( Seligman, 2002 ). Particularly, Buddhism supports “the cultivation of happiness, genuine inner transformation, deliberately selecting and focusing on positive mental states” ( Lama and Cutler, 2008 ). In addition, mindfulness has been shown to be positively related to happiness ( Shultz and Ryan, 2015 ), contributing to eudemonic and hedonic well-being ( Howell et al., 2011 ).

In fact, although the definition of happiness has a long history and goes back to philosophical arguments and the search for practical wisdom, in modern times, happiness has been equated with hedonism. It relies on the achievement of immediate pleasure, on the absence of negative affect, and on a high degree of satisfaction with one's life ( Argyle et al., 1999 ). Nonetheless, scholars now argue that authentic subjective well-being goes beyond this limited view and support an interpretation of happiness as a eudemonic endeavor ( Ryff, 1989 ; Keyes, 2006 ; Seligman, 2011 ; Hone et al., 2014 ). Within this view, individuals seem to focus more on optimal psychological functioning, living a deeply satisfying life and actualizing their own potential, personal growth, and a sense of autonomy ( Deci and Ryan, 2008 ; Ryff, 2013 ; Vazquez and Hervas, 2013 ; Ivtzan et al., 2016 ). In psychology, such a view finds one of its primary supports in Maslow's (1981) theory of human motivation. Maslow argued that experience of a higher degree of satisfaction derives from a more wholesome life conduct. In Maslow's hierarchy of needs theory, once lower and more localized needs are satisfied, the unlimited gratification of needs at the highest level brings people to a full and deep experience of happiness ( Inglehart et al., 2008 ). Consequently, today, several scholars argue that high levels of subjective well-being depend on a multi-dimensional perspective, which encompasses both hedonic and eudemonic components ( Huta and Ryan, 2010 ; Ryff and Boylan, 2016 ). Under a wider perspective, the process of developing well-being reflects the notion that mental health and good functioning are more than a lack of illness ( Keyes, 2005 ). This approach is especially evident if we consider that even the definition of mental health has been re-defined by the World Health Organization (1948) , which conceives health not merely as the absence of illness, but as a whole state of biological, psychological, and social well-being.

To date, evidence exists suggesting that happiness is, in some extent, modulable and trainable. Thus, simple cognitive and behavioral strategies that individuals choose in their lives could enhance happiness ( Lyubomirsky et al., 2005 ; Sin and Lyubomirsky, 2009 ). In the history of psychology, a multitude of clinical treatments have been applied to minimize the symptoms of a variety of conditions that might hamper people from being happy, such as anger, anxiety, and depression (for instance, see Forman et al., 2007 ; Spinhoven et al., 2017 ). In parallel with this view, an alternative—and less developed—perspective found in psychology focuses on the scientific study of individual experiences and positive traits, not for clinical ends, but instead for personal well-being and flourishing (e.g., Fredrickson and Losada, 2005 ; Sin and Lyubomirsky, 2009 ). Yet, the question of exactly how to foster subjective well-being and happiness, given its complexity and importance, remains open to research. Answering this question is of course of pivotal importance, both individually and at the societal level. Positive Psychology Interventions encompass simple, self-administered cognitive behavioral strategies intended to reflect the beliefs and behaviors of individuals and, in response to that, to increase the happiness of the people practicing them ( Sin and Lyubomirsky, 2009 ; Hone et al., 2015 ). Specifically, a series of comprehensive psychological programs to boost happiness exist, such as Fordyce's program ( Fordyce, 1977 ), Well-Being Therapy ( Fava, 1999 ), and Quality of Life Therapy ( Frisch, 2006 ). Similarly, a variety of meditation-based programs aim to develop mindfulness and emotional regulatory skills ( Carmody and Baer, 2008 ; Fredrickson et al., 2008 ; Weytens et al., 2014 ), such as Mindfulness-Based Stress Reduction (MBSR; Kabat-Zinn, 1990 ) and Mindfulness-Based Cognitive Therapy (MBCT; Teasdale et al., 2000 ). Far from being a mere trend ( De Pisapia and Grecucci, 2017 ), those mindfulness-based interventions have been shown to lead to increased well-being ( Baer et al., 2006 ; Keng et al., 2011 ; Choi et al., 2012 ; Coo and Salanova, 2018 ; Lambert et al., 2019 ) in several domains, such as cognition, consciousness, self, and affective processing ( Raffone and Srinivasan, 2017 ). Typically, mindfulness programs consist of informal and formal practice that educate attention and develop one's capacity to respond to unpredicted and/or negative thoughts and experiences ( Segal and Teasdale, 2002 ). In this context, individuals are gradually introduced to meditation practices, focusing first on the body and their own breath, and later on thoughts and mental states. The effects of these programs encompass positive emotions and reappraisal ( Fredrickson et al., 2008 ; Grecucci et al., 2015 ; Calabrese and Raffone, 2017 ) and satisfaction in life ( Fredrickson et al., 2008 ; Kong et al., 2014 ) and are related to a reduction of emotional reactivity to negative affect, stress ( Arch and Craske, 2006 ; Jha et al., 2017 ), and aggressive behavior ( Fix and Fix, 2013 ). All these effects mediate the relationship between meditation frequency and happiness ( Campos et al., 2016 ). This allows positive psychology interventions to improve subjective well-being and happiness and also reduce depressive symptoms and negative affect along with other psychopathologies ( Seligman, 2002 ; Quoidbach et al., 2015 ). Engaging in mindfulness might enhance in participants the awareness of what is valuable to them ( Shultz and Ryan, 2015 ). This aspect has been related to the growth of self-efficacy and autonomous functioning and is attributable to an enhancement in eudemonic well-being ( Deci and Ryan, 1980 ). Moreover, being aware of the present moment provides a clearer vision of the existing experience, which in turn has been associated with increases in hedonic well-being ( Coo and Salanova, 2018 ). Following these approaches, recent research provides evidence that trainings that encompass both hedonic and eudemonic well-being are correlated with tangible improved health outcomes ( Sin and Lyubomirsky, 2009 ).

Although there is a consistent interest in scientific research on the general topic of happiness, such studies present several limitations. Firstly, most of the research has focused on clinical studies to assess the effectiveness of happiness-based interventions—in line with more traditional psychological research, which is primarily concerned with the study of mental disorders ( Garland et al., 2015 , 2017 ; Groves, 2016 ). Secondly, most of the existing interventions are narrowly focused on the observation of single dimensions (i.e., expressing gratitude or developing emotional regulation skills) ( Boehm et al., 2011 ; Weytens et al., 2014 ). Moreover, typically studies involve brief 1- to 2-week interventions ( Gander et al., 2016 ), in contrast with the view that eudemonia is related to deep and long-lasting aspects of one's personal lifestyle. Furthermore, while the effectiveness of mindfulness-based therapies is well-documented, research that investigates the effects of mindfulness retreats has been lacking, which are characterized by the involvement of more intense practice from days to even years [for meta-analysis and review, see Khoury et al. (2017) , McClintock et al. (2019) , Howarth et al. (2019) ].

In this article, we report the effects on subjective well-being of an integrated mental training program called The Art of Happiness , which was developed and taught by two of the authors (CM for the core course subject matter and NDP for the scientific presentations). The course lasted 9 months and included three different modules (see Methods and Supplementary Material for all details), namely, seven weekends (from Friday evening to Sunday afternoon) dedicated to a wide range of specific topics, two 5-day long retreats, and several free activities at home during the entire period. The course was designed to help practitioners develop new ways to nurture their own happiness, cultivating both self-awareness and their openness to others, thereby fostering their own emotional and social well-being. The basic idea was to let students discover how the union of ancient wisdom and spiritual practices with scientific discoveries from current neuropsychological research can be applied beneficially to their daily lives. This approach and mental training program was inspired by a book of the Fourteenth Dalai Lama Tenzin Gyatso and the psychiatrist Lama and Cutler (2008) . The program rests on the principle that happiness is inextricably linked to the development of inner equilibrium, a kinder and more open perspective of self, others, and the world, with a key role given to several types of meditation practices. Additionally, happiness is viewed as linked to a conceptual understanding of the human mind and brain, as well as their limitations and potentiality, in the light of the most recent scientific discoveries. To this end, several scientific topics and discoveries from neuropsychology were addressed in the program, with a particular focus on cognitive, affective, and social neuroscience. Topics were taught and discussed with language suitable for the general public, in line with several recent books (e.g., Hanson and Mendius, 2011 ; Dorjee, 2013 ; Goleman and Davidson, 2017 ). The aim of this study was to examine how several psychological measures, related to psychological well-being, changed among participants in parallel with course attendance and meditation practices. Given the abovementioned results of the positive effects on well-being ( Baer et al., 2006 ; Fredrickson et al., 2008 ; Keng et al., 2011 ; Choi et al., 2012 ; Kong et al., 2014 ; Coo and Salanova, 2018 ; Lambert et al., 2019 ), we predicted to find a significant increase in the dimensions of life satisfaction, control of anger, and mindfulness abilities. Conversely, we expected to observe a reduction of negative emotions and mental states ( Arch and Craske, 2006 ; Fix and Fix, 2013 ; Jha et al., 2017 )—i.e., stress, anxiety and anger. Moreover, our aim was to explore how those measures changed during the course of the mental training program, considering not only the general effects of the course (longitudinal effects) but also specific effects within each retreat (short-term effects). Our expectation for this study was therefore that the retreats would have had an effect on the psychological dimensions of well-being linked to the emotional states of our participants, while the whole course would have had a greater effect on the traits related to well-being. The conceptual distinction between states and traits was initially introduced in regard to anxiety by Cattell and Scheier (1961) , and then subsequently further elaborated by Spielberger et al. (1983) . When considering a mental construct (e.g., anxiety or anger), we refer to trait as a relatively stable feature, a general behavioral attitude, which reflects the way in which a person tends to perceive stimuli and environmental situations in the long term ( Spielberger et al., 1983 ; Spielberger, 2010 ). For example, subjects with high trait anxiety have indeed anxiety as a habitual way of responding to stimuli and situations. The state, on the other hand, can be defined as a temporary phase within the emotional continuum, which, for example, in anxiety is expressed through a subjective sensation of tension, apprehension, and nervousness, and is associated with activation of the autonomic nervous system in the short term ( Spielberger et al., 1983 ; Saviola et al., 2020 ). Here, in the adopted tests and analyses, we keep the two time scales separated, and we investigate the results with the aim of understanding the effects of the program on states and traits of different emotional and well-being measures. As a first effect of the course, we expect that the retreats affect mostly psychological states (as measured in the comparison of psychological variables between start and end of each retreat), whereas the full course is predicted to affect mainly psychological traits (as measured in the comparison of the psychological variables between start, middle, and end of the entire 9-month period).

Materials and Methods

Participants.

The participants in the mental training program and in the related research were recruited from the Institute Lama Tzong Khapa (Pomaia, Italy) in a 9-month longitudinal study (seven modules and two retreats) on the effects of a program called The Art of Happiness (see Supplementary Material for full details of the program). Twenty-nine participants followed the entire program (there were nine dropouts after the first module). Their mean age was 52.86 years (range = 39–66; SD = 7.61); 72% were female. Participants described themselves as Caucasian, reaching a medium-high scholarly level with 59% of the participants holding an academic degree and 41% holding a high school degree. The participants were not randomly selected, as they were volunteers in the program. Most of them had no serious prior experience of meditation, only basic experience consisting of personal readings or watching video courses on the web, which overall we considered of no impact to the study. The only exclusion criteria were absence of a history of psychiatric or neurological disease, and not being currently on psychoactive medications. The study was approved by the Ethics Committee of the Sapienza University of Rome, and all participants gave written informed consent. The participants did not receive any compensation for participation in the study.

The overall effectiveness of the 9-month training was examined using a within-subjects design, with perceived stress, mindfulness abilities, etc. (Time: pre–mid–end) as the dependent variable. The effectiveness of the retreats was examined using a 2 × 2 factor within-subjects design (condition: pre vs. post; retreat: 1 vs. 2), with the same dependent variables. The specific contemplative techniques that were applied in the program are described in the Supplementary Material , the procedure is described in the Procedure section, and the measurements are described in the Materials section.

Mental Training Program

The program was developed and offered at the Institute Lama Tzong Khapa (Pomaia, Italy). It was one of several courses that are part of the Institute's ongoing programs under the umbrella of “Secular Ethics and Universal Values.” These various programs provide participants with opportunities to discover how the interaction of ancient wisdom and spiritual practices with contemporary knowledge from current scientific research in neuropsychology can be applied extensively and beneficially to improve the quality of their daily lives.

Specifically, The Art of Happiness was a 9-month program, with one program activity each month, either a weekend module or a retreat; there were two retreats—a mid-course retreat and a concluding retreat (for full details on the program, see Supplementary Material ). Each thematic module provided an opportunity to sequentially explore the topics presented in the core course text, The Art of Happiness by the Lama and Cutler (2008) .

In terms of the content of this program, as mentioned above, the material presented and explored has been drawn on the one hand from the teachings of Mahayana Buddhism and Western contemplative traditions, and current scientific research found in neuropsychology on the other hand. On the scientific side, topics included the effects of mental training and meditation, the psychology and neuroscience of well-being and happiness, neuroplasticity, mind–brain–body interactions, different areas of contemplative sciences, the placebo effects, the brain circuits of attention and mind wandering, stress and anxiety, pain and pleasure, positive and negative emotions, desire and addiction, the sense of self, empathy, and compassion (for a full list of the scientific topics, see Supplementary Material ).

The overall approach of the course was one of non-dogmatic exploration. Topics were presented not as undisputed truths, but instead as information to be shared, explored, examined, and possibly verified by one's own experience. Participants were heartily invited to doubt, explore, and test everything that was shared with them, to examine and experience firsthand whether what was being offered has validity or not.

The course was, essentially, an informed and gentle training of the mind, and in particular of emotions, based on the principle that individual well-being is inextricably linked to the development of inner human virtues and strengths, such as emotional balance, inner self-awareness, an open and caring attitude toward self and others, and clarity of mind that can foster a deeper understanding of one's own and others' reality.

The program provided lectures and discussions, readings, and expert videos introducing the material pertinent to each module's topic. Participants engaged with the material through listening, reading, discussing, and questioning. Participants were provided with additional learning opportunities to investigate each topic more deeply, critically, and personally, through the media of meditation, journaling, application to daily life, exercises at home, and contemplative group work with other participants in dyads and triads. Participants were then encouraged to reflect repeatedly on their insights and on their experiences, both successful and not, to apply their newly acquired understandings to their lives, by incorporating a daily reflection practice into their life schedule. The two program retreats also provided intensive contemplative experiences and activities, both individual and in dialogue with others.

On this basis, month after month in different dedicated modules, participants learned new ways to nurture their own happiness, to cultivate their openness to others, to develop their own emotional and social well-being, and to understand some of the scientific discoveries on these topics.

The specific topics addressed in corresponding modules and retreats, each in a different and consecutive month, were as follows: (1) The Purpose of Life: Authentic Happiness; (2) Empathy and Compassion; (3) Transforming Life's Suffering; (4) Working with Disturbing Emotions I: Hate and Anger; first retreat (intermediate); (5) Working with Disturbing Emotions II: The Self Image; (6) Life and Death; (7) Cultivating the Spiritual Dimension of Life: A Meaningful Life; second retreat (final). Full details of the entire program are reported in the Supplementary Material .

Participants were guided in the theory and practice of various contemplative exercises throughout the course pertaining to all the different themes. Recorded versions of all the various meditation exercises were made available to participants, enabling them to repeat these practices at home at their own pace.

Participants were encouraged to enter the program already having gained some basic experience of meditation, but this was not a strict requirement. In fact, not all participants in this experiment actually fulfilled this (only five), although each of the other participants had previous basic experiences of meditation (through personal readings, other video courses, etc.). In spite of this variety, by the end of the 9-month program, all participants were comfortable with contemplative practices in general and more specifically with the idea of maintaining a meditation practice in their daily lives.

During the various Art of Happiness modules, a variety of basic attentional and mindful awareness meditations were practiced in order to enhance attentional skills and cultivate various levels of cognitive, emotional, social, and environmental awareness.

Analytical and reflective contemplations are a form of deconstructive meditation ( Dahl et al., 2015 ), which were applied during the course in different contexts. On the one hand, these types of meditations were applied in the context of heart-opening practices—for example, in the cultivation of gratitude, forgiveness, loving-kindness toward self and others, self-compassion, and compassion for others. Analytical and reflective meditations were also practiced as a learning tool for further familiarization with some of the more philosophical subject matter of the course—engaging in a contemplative analysis of impermanence (for example, contemplating more deeply and personally the transitory nature of one's own body, of one's own emotions and thoughts, as well as of the material phenomena that surround us). These analytical meditations were also accompanied by moments of concentration (sustained attention) at the conclusion of each meditation focusing on what the meditator has learned or understood in the meditative process, in order to stabilize and reinforce those insights more deeply within the individual.

Additional contemplative activities were also included in the program: contemplative art activities, mindful listening, mindful dialogue, and the practice of keeping silence during the retreat. Participants were, in addition, encouraged to keep a journal of their experiences during their Art of Happiness journey, especially in relation to their meditations and the insights and questions that emerged within themselves, in order to enhance their self-awareness and cultivate a deeper understanding of themselves, their inner life and well-being, and their own inner development during the course and afterward.

During the two retreats, the previous topics were explored again (modules 1–4 for the intermediary retreat and modules 5–7 for the final retreat), but without discussing the theoretical aspects (i.e., the neuroscientific and psychological theories), instead only focusing on the contemplative practices, which were practiced extensively for the whole day, both individually and in group activities (for a full list of the contemplative practices and retreat activities, see Supplementary Material ).

We collected data at five-time points, always during the first day (either of the module or the retreat): at baseline (month 1 - T0), at pre (T1) and post (P1) of the mid-course retreat (month 5–Retreat 1), and at pre (T2) and post (R2) of the final retreat (month 9–Retreat 2), as shown in Figure 1 . Participants filled out the questionnaires on paper all together within the rooms of the Institute Lama Tzong Khapa at the beginning of each module or retreat, and at the end of the retreats, with the presence of two researchers. The order of the questionnaires was randomized, per person and each questionnaire session lasted less than an hour.

Figure 1 . The timing of the course and the experimental procedure, including the modules, the retreats, and the 5 data collections (from T0 to P2).

The adopted questionnaires were those commonly used in the literature to measure a variety of traits and states linked to well-being. An exhaustive description of the self-reported measures follows below.

Satisfaction With Life Scale (SWLS)

The SWLS ( Diener et al., 1985 ) was developed to represent cognitive judgments of life satisfaction. Participants indicated their agreement in five items with a seven-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree). Scores range from 5 to 35, with higher scores representing higher levels of satisfaction. Internal consistency is very good with Cronbach's α = 0.85 [Italian version of the normative data in Di Fabio and Palazzeschi (2012) ].

Short Version of the Perceived Stress Scale (PSS-10)

The PSS ( Cohen et al., 1983 ) was designed to assess individual perception and reaction to stressful daily-life situations. The questionnaire consists of 10 questions related to the feelings and thoughts of the last month, with a value ranging from 0 (never) to 4 (very often) depending on the severity of the disturbance caused. Scores range from 0 to 40. Higher scores represent higher levels of perceived stress, reflecting the degree to which respondents find their lives unpredictable or overloaded. Cronbach's α ranges from 0.78 to 0.93 [Italian version of the normative data by Mondo et al. (2019) ].

State-Trait Anxiety Inventory (STAI)

The STAI ( Spielberger et al., 1983 ) was developed to assess anxiety. It has 40 items, on which respondents evaluate themselves in terms of frequency with a four-point Likert scale ranging from 1 (almost never) to 4 (almost always). The items are grouped in two independent subscales of 20 items each that assess state anxiety, with questions regarding the respondents' feelings at the time of administration, and trait anxiety, with questions that explore how the participant feels habitually. The scores range from 20 to 80. Higher scores reflect higher levels of anxiety. Internal consistency coefficients for the scale ranged from 0.86 to 0.95 [Italian version of the normative data by Spielberger et al. (2012) ].

Positive and Negative Affect Schedule (PANAS)

PANAS ( Watson et al., 1988 ) measures two distinct and independent dimensions: positive and negative affect. The questionnaire consists of 20 adjectives, 10 for the positive affect subscale and 10 for the negative affect scale. The positive affect subscale reflects the degree to which a person feels enthusiastic, active, and determined while the negative affect subscale refers to some unpleasant general states such as anger, guilt, and fear. The test presents a five-point Likert scale (1 = very slightly or not at all; 5 = extremely). The alpha reliabilities are acceptably high, ranging from 0.86 to 0.90 for positive affect and from 0.84 to 0.87 for negative affect [Italian version of the normative data by Terracciano et al. (2003) ].

Five Facet Mindfulness Questionnaire (FFMQ)

The FFMQ ( Baer et al., 2008 ) was developed to assess mindfulness facets through 39 items rated on a five-point Likert scale, ranging from 1 (never or very rarely true) to 5 (very often or always true). A total of five subscales are included: attention and observation of one's own thoughts, feelings, perceptions, and emotions ( Observe ); the ability to describe thoughts in words, feelings, perceptions, and emotions ( Describe ); act with awareness, with attention focused and sustained on a task or situation, without mind wandering ( Act-aware ); non-judgmental attitude toward the inner experience ( Non-Judge ); and the tendency to not react and not to reject inner experience ( Non-React ). Normative data of the FFMQ have demonstrated good internal consistency, with Cronbach's α ranging from 0.79 to 0.87 [Italian version by Giovannini et al. (2014) ].

State-Trait Anger Expression Inventory-2 (STAXI-2)

The STAXI-2 ( Spielberger, 1999 ) provides measures to assess the experience, expression, and control of anger. It comprises 57 items rated on a four-point Likert scale, ranging from 0 (not at all) to 3 (very much indeed). Items are grouped by four scales: the first, State Anger scale, refers to the emotional state characterized by subjective feelings and relies on three more subscales: Angry Feelings, Physical Expression of Anger, and Verbal Expression of Anger. The second scale is the Trait Anger and indicates a disposition to perceive various situations as annoying or frustrating with two subscales—Angry Temperament and Angry Reaction. The third and last scales are Anger Expression and Anger Control. These assess anger toward the environment and oneself according to four relatively independent subscales: Anger Expression-OUT, Anger Expression-IN, Anger Control-OUT, and Anger Control-IN. Alpha coefficients STAXI-2 were above 0.84 for all scales and subscales, except for Trait Anger Reaction, which had an alpha coefficient of 0.76 [Italian version by Spielberger (2004) ].

Statistical Analysis

The responses on each questionnaire were scored according to their protocols, which resulted in one score per participant and a time point for each of the 22 scale/subscale questionnaires examined. Missing values (<2%) were imputed using the median. Descriptive statistics for all variables were analyzed and are summarized in Table 1 and in the first panel (column) of Figures 2 – 5 . Prior to conducting primary analyses, the distribution of scores on all the dependent variables was evaluated. Because the data were not normally distributed, we used non-parametric tests. Permutation tests are non-parametric tests as they do not rely on assumptions about the distribution of the data and can be used with different types of scales and with a small sample size.

Table 1 . Descriptive statistics of the depended variables among time points.

Figure 2 . Results of the Satisfaction with Life Scale (SWLS), Perceived Stress Scale (PSS), State and Trait Anxiety Index (STAI), and Positive and Negative Affect Scales (PANAS). The first (left) panel depicts pooled mean raw data per time point estimating 95% confidence interval. The second (central) panel represents changes in pooled mean ( y -axis) between retreats. The solid line represents retreat 1 and the dotted line denotes retreat 2 derived from the contrasts of the two-way ANOVA. The third (right) panel depicts bar charts representing the changes in mean between the 3 time points derived from the one-way ANOVA. Note that scores are on the y -axis and time is on the x -axis. Time points legend: baseline (month 1—T0), pre (T1), post (P1), mid-course retreat (month 5—retreat 1), pre (T2), and post (R2) of the final retreat (month 9—retreat 2). Statistical significance, * p < 0.05.

Figure 3 . Results for the Five Facet Mindfulness Questionnaire FFMQ (Observe, Describe, Act with Awareness, Non-judge, and Non-react). The first (left) panel depicts pooled mean raw data per time point estimating 95% confidence interval. The second (central) panel represents changes in pooled mean ( y -axis) between retreats. The solid line represents retreat 1 and the dotted line denotes retreat 2 derived from the contrasts of the two-way ANOVA. The third (right) panel depicts bar charts representing the changes in mean between the 3 time points derived from one-way ANOVA. Note that scores are on the y -axis and time id on the x -axis. Time points legend: baseline (month 1—T0), pre (T1), post (P1), mid-course retreat (month 5—retreat 1), pre (T2), and post (P2) of the final retreat (month 9—retreat 2). Statistical significance, * p < 0.05.

Figure 4 . Results of the first part of the State Trait Anger Expression Inventory (STAXI-2): State Anger, State Anger Feelings, State Anger Physical, State Anger Verbal, Trait Anger, and Trait Anger Temperament. The first (left) panel depicts pooled mean raw data per time point estimating 95% confidence interval. The second (central) panel represents changes in pooled mean ( y -axis) between retreats. The solid line represents retreat 1 and the dotted line denotes retreat 2 derived from the contrasts of the two-way ANOVA. The third (right) panel depicts bar charts representing the changes in mean between the 3 time points derived from one-way ANOVA. Note that scores are on the y -axis and time is on the x -axis. Time points legend: baseline (month 1—T0), pre (T1), post (P1), mid-course retreat (month 5—retreat 1), pre (T2), and post (R2) of the final retreat (month 9—retreat 2). Statistical significance, ** p < 0.01 and * p < 0.05.

Figure 5 . Results from the second part of the State Trait Anger Expression Inventory (STAXI-2): Trait Anger Reaction, Anger Expression-IN, Anger Expression-OUT, Anger Control-IN, and Anger Control OUT. The first (left) panel depicts pooled mean raw data per time point estimating 95% confidence interval. The second (central) panel represents changes in pooled mean ( y -axis) between retreats. The solid line represents retreat 1 and the dotted line denotes retreat 2 derived from the contrasts of the two-way ANOVA. The third (right) panel depicts bar charts representing the changes in mean between the 3 time points derived from one-way ANOVA. Note that scores are on the y -axis and time is on the x -axis. Time points legend: baseline (month 1—T0), pre (T1), post (P1), mid-course retreat (month 5—Retreat 1), pre (T2), and post (R2) of the final retreat (month 9—Retreat 2). Statistical significance, * p < 0.05.

The longitudinal effects of the program were analyzed to determine whether scores changed between the start, mid-point (5 months), and the end (9 months) of the course. To achieve this, we compared the main effect of the program on the score , considering Time as a unique factor with three levels: at the baseline (T0), at the pre of the mid-retreat (T1), and at the pre of the final retreat (T2). Here, we used a one-way permutation Repeated Measures Analysis of Variance (RM ANOVA) with the aovperm() function from the Permuco package v. 1.0.2 in R ( Frossard and Renaud, 2018 ), which implements a method from Kherad-Pajouh and Renaud (2014) . The difference between the traditional and the permutation ANOVA is that, while the traditional ANOVA tests the equality of the group mean, the permutation version tests the exchangeability of the group observations. In this study, the number of permutations was set to 100,000 and the alpha level was set to 0.05; therefore, the p -value was computed as the ratio between the number of permutation tests that have an F value higher than the critical F value and the number of permutations performed. Effect size estimates were calculated using partial eta squared. Post hoc testing used pairwise permutational t -tests with the “pairwise.perm.t.test” function from the “RVAideMemoire” package in R ( Hervé and Hervé, 2020 ). To account for Type I errors introduced by multiple pairwise tests and Type II errors introduced by small sample size, we applied the false discovery rate (FDR) correction method of Benjamini and Hochberg (1995) and set statistical significance at p = 0.05. Results are summarized in Table 2 and in the third panel (column) of Figures 2 – 5 .

Table 2 . One-way ANOVA and pairwise comparison results with 100,000 permutations.

The short-term effects of the contemplative program on each retreat were analyzed to determine whether scores changed post-retreats and whether these changes occurred in both retreats. Thus, we used a two-way permutation RM ANOVA, with the score of each scale/subscale as the dependent variable and the within-subject factors Retreat (1, 2) and Condition (Pre T1/T2, Post P1/P2) as independent variables. Results are summarized in Table 3 and in the second panel (column) of Figures 2 – 5 .

Table 3 . Results of the two-way permutation RM ANOVAs.

In addition, we explored differences attributed to the course and to the retreats using a paired permutation t test with the “perm.t.test()” function in R. We compare those psychological measures at the beginning of the course (T0) with its very end (P2), which coincided with the end of the second retreat. In this way, we illustrate a summary of changes due both to the second retreat and to the whole course. The results are summarized in Table 4 and depicted in a radar plot in Figure 6 .

Table 4 . Overall changes between the start (T0) and the end of the course (P2).

Figure 6 . Results of the permutation t -test between the start and the end of the course. All values ranged from 0 to 1. Variables: SWLS, Satisfaction with Life Scale; S-Ang/F, Feeling Angry; S-Ang/V, Feel like Expressing Anger Verbally; S-Ang/P, Feel like Expressing Anger Physically; T-Ang/T, Angry Temperament; T-Ang/R, Angry reaction; AX-O, Anger Expression-OUT; AX-I, Anger Expression-IN; AC-O, Anger Control-OUT; AC-I, Anger Control-IN; PSS, Perceived Stress Scale; STAI-Y1, State-Trait Anxiety Inventory—State; STAI-Y2, State-Trait Anxiety Inventory—Trait; PA and NA, Positive and Negative Affect Scales, respectively; OBS, Observe; DES, Describe; AWA, Act with awareness, Njudge, Non-judge; NReact, Non-react. To make consistent that an increase of the specific scale corresponds to an improvement in well-being, negative scales were reversed, namely: PSS, STAI-Y1, STAI-Y2, PANAS-NA, S-Ang, S-Ang/F, S-Ang/P, S-Ang/V, T-Ang, T-Ang/T, S-Ang/R, AX-O, AX-I. Concerning the statistical significance, *** p < 0.001, ** p < 0.01, and * p < 0.05.

Effects of the Program

Results from one-way permutation RM ANOVA showed a statistically significant effect of the program on SWLS at the p = 0.008 level over the Time course factor with a large effect size (ηp 2 = 0.16). Post hoc analysis revealed that the SWLS score was significantly higher at T2 with respect to T2 (mean difference = 2.48; p = 0.016). Similarly, SWLS was higher T2 as compared to T1 (mean difference = 1.38; p = 0.032).

Results also provided statistically significant evidence of changes in the PSS over the Time course ( p = 0.009), showing a large effect size (ηp 2 = 0.16). Post-hoc results showed a difference between T0 and T1, revealing that the PSS was significantly lower at T1 (mean difference = −2, p = 0.02).

Results revealed a significant effect of the Time course for Trait Anxiety ( p = 0.009, ηp 2 = 0.16). Post-hoc tests revealed a reduction in Trait Anxiety from the start of the course (T0) to the first day of the second retreat (T2) (M diff. = −3.21, p = 0.25).

Results also showed a significant effect of the Time course for negative affect ( p = 0.004, ηp 2 = 0.19). Post hoc analysis revealed that contemplative practice led to a reduction in negative affect from the baseline (T0) to the first day of the first retreat (T1) (mean difference = −2.42) and between T0 and first day of the second retreat (T2) (mean difference = −2.92), which differed significantly with p = 0.021 and p = 0.012, respectively.

Moreover, a significant effect of the Time course was found for several subscales of the FFMQ. First, the observe scale was found at the p = 0.023 level showing a large effect size (ηp 2 = 0.13). Post-hoc comparisons revealed an increasing capacity to observe one's own thoughts, from the middle of the course (T1) to the first day of the second retreat (T2) (mean difference = 1.58, p = 0.038). Likewise, there was a significant difference for the capacity to Act with Awareness ( p = 0.036, ηp 2 = 0.12). Post hoc comparisons revealed an increased level at T2 as compared to T1 (mean difference = 2.07, p = 0.043). The Time course had a significant effect on the Non-Judge subscale with a large effect size ( p = 0.002, ηp 2 = 0.20). Post hoc analysis indicated a significant increase from T0 to T1 (mean difference = 2.07, p = 0.013), as well as from T0 to T2 (mean difference = 3.31, p = 0.013).

In regard to the STAXI-2, we found Time course significant effects on Trait Anger ( p = 0.001, ηp 2 = 0.23) and its subscales, Trait Anger Temperament ( p = 0.001, ηp 2 = 0.22) and Trait Anger Reaction ( p = 0.016, ηp 2 = 0.14). Post-hoc comparisons revealed a significance difference on the Trait Anger Scale, which decreased from the beginning of the course (T0) to 5 months later (T1) (mean difference = −1.83, p = 0.041) and also from T0 to the end of the course (T2) (mean difference = −3.24, p = 0.002). Similarly, State Anger Temperament significantly decreased from T0 to T1 (mean difference = −0.79, p = 0.016) and from T0 to T2 (mean difference = −1.38, p = 0.008). Additionally, Trait Anger Reaction decreased from T0 to T2 (mean difference = −1.24, p = 0.023). Finally, the longitudinal effect of the course on the STAXI-2 led to significant results in the Anger Control-IN subscale over the Time course ( p = 0.03, ηp 2 = 0.12). Here, post-hoc comparisons showed a statistically significant difference between T0 and T2, which increased (mean difference = 1.76, p =.044). For more details, see Table 2 and the third panel (column) of Figures 2 – 5 .

Effects of the Retreats

Two-way permutation RM ANOVAs showed a significant main effect for Retreat on SWLS ( p = 0.002, ηp 2 = 0.16), Trait Anxiety ( p = 0.001, ηp 2 = 0.19), positive affect ( p = 0.044, ηp 2 = 0.07), Observe ( p = 0.008, ηp 2 = 0.12), Act with awareness ( p ≤ 0.001, ηp 2 = 0.22), Non-Judge ( p = 0.045, ηp 2 =.07), Non-React ( p = 0.02, ηp 2 = 0.10), Trait Anger ( p = 0.008, ηp 2 = 0.12), Trait Anger Temperament ( p = 0.022, ηp 2 = 0.09), Trait Anger Reaction ( p = 0.019, ηp 2 = 0.10), and Anger Control-IN ( p = 0.029, ηp 2 = 0.08). A main effect of the Condition (Pre vs. Post) was found only for the State Anxiety scale with p = 0.004 and a large effect size (ηp 2 = 0.14). Analysis results including F statistics are summarized in Table 3 ; a visual representation of the data is presented in the second panel (column) of Figures 2 – 5 .

Overall Effects of the Course and Retreats

As predicted, permutation t -test analysis revealed that participants increased their reported level of SWLS from the start (T0) to the end (P2) of the course (mean difference = 2.83, p = 0.008). Two subscales from the FFMQ, namely, the capacity to observe one's own thoughts (mean difference = 1.86, p = 0.039) and non-judgmental attitude toward the inner experience (mean difference = 3.24, p = 0.006), also significantly increased from the start to the end of the course. On the other hand, the affect linked to the progression from the start (T0) to the very end of the course (P2) was related to a significant decrease in the negative affect (mean difference = −3.62, p = 0.001). In the same way, the average level of stress of the sample decreased significantly (mean difference = −1.9, p = 0.033) along with a significant decrease of Trait Anxiety (M diff = −3.97, p ≤ 0.001). Participants also decreased on almost all STAXI-2 subscales. Here, the results from permutation paired t -test reveal a significant difference in scores, which decreased from T0 to P2 on all the subscales of Trait Anger (mean difference = −3.55, p ≤ 0.001; Trait Anger Temperament: mean difference = −1.34, p ≤ 0.001; Trait Anger Reaction: mean difference = −1.52, p ≤ 0.001), with an increased value for the subscales Anger Control-OUT (mean difference = 1.93, p ≤ 0.009) and Anger Control-IN (mean difference = 1.93, p = 0.017). For more details, see Table 4 and Figure 6 .

The aim of this study was to examine the effectiveness of an integrated 9-month mental training program called The Art of Happiness , which was developed to increase well-being in a general population. By a range of well-established psychometric assessment tools, we quantified how several psychological well-being variables changed with course attendance. We took into account both the trait effects of the course acting at a long timescale (over the 9-month duration of the full course) and the state effects of intensive retreat experiences acting at a short time scale (over the course of each of the two retreats). Several psychological well-being measures related to states and—more importantly—traits gradually improved as participants progressed from the beginning to the end of the course.

On the one hand, the program produced a significant longitudinal effect (9 months) revealing a progressive increase in the volunteer's levels of life satisfaction and of the capacities to reach non-judgmental mental states, to act with awareness, to non-react to inner experience, and to exercise control over attention to the internal state of anger, in line with other contemplative interventions ( Fredrickson et al., 2008 ; Keng et al., 2011 ; Baer et al., 2012 ; Kong et al., 2014 ). Conversely, after the completion of the program, there were decreases in levels of trait anxiety, trait anger (including both the anger temperament and reaction subscales), and negative affect, showing a progressive reduction during the intervention. These results support prior research that demonstrated the longitudinal positive effects of a multitude of contemplative practices on well-being measures linked to—among others—decreased trait anxiety, trait anger, and negative affect ( Fix and Fix, 2013 ; Khoury et al., 2015 ; Gotink et al., 2016 ). Such findings highlight the gradual development of mental states related to subjective well-being in parallel with ongoing contemplative practices over a time scale of months, with a gradual increase of wholesome mental states, and a gradual decrease of unwholesome mental states. Notably, as in other mindfulness interventions ( Khoury et al., 2015 ; Gotink et al., 2016 ), there was a significant reduction in the level of perceived stress already in the first few months of the program (T0–T1).

Additionally, these results show the specific effects between retreat experiences within the program as an intervention for fostering happiness. Specifically, the retreats had a positive effect on the participants' perceived well-being, which improved between the two retreats (with a 4-month interval). Among other assessed dimensions, between the retreats, there were significantly increased levels of life satisfaction, positive affect, and mindful abilities to act with awareness, to observe, non-react, and non-judge inner experience and the capacity to control anger toward oneself. Conversely, there were significantly lower levels of trait anxiety and trait anger (including both the anger temperament and reaction subscales) between the retreats (over a period of 4 months).

Regarding the very short effects of the course, we highlight significant changes within the first part of the training and prior to the first retreat (T0–T1). Here, some variables related to happiness changed most, suggesting their independence from retreat. Particularly, PSS notably decreased along with negative affect and Trait Anger (the subscale of Angry Temperament), while the capacity of non-judgmental attitude toward the inner experience significantly increased, providing useful information for future interventions.

Moreover, participants' state anxiety significantly decreased in a very short time (5 days), between pre and post of both retreats. These findings are consistent with previous studies, which demonstrated the positive effects of contemplative training and practices on these measures in retreats ( Khoury et al., 2017 ; Howarth et al., 2019 ; McClintock et al., 2019 ). In Figure 6 , we make a general and integrated comparison between the various psychological measures, comparing the very beginning of the course with its very end, which also coincided with the end of the second retreat. In this way, we illustrate both state changes (due to the second retreat) and trait changes (due to the whole course). This representation allows an integrated view of all the changes that took place at different time scales. This graph might suggest that the only measures that did not change significantly from the beginning to the end of the course are those in which the participants already had a score strongly oriented toward well-being, and therefore with little room for a change. Thus, future studies could take into account individual differences when evaluating happiness programs.

Although the present findings are promising, this study presents several limitations that need to be taken into consideration. The two main limitations rely on the absence of a randomized control group and in the fact that participants were self-selected. This lack of verification makes it difficult to determine whether the results are attributable to the program or to other factors, for example, simply arising due to spending time in a happiness-oriented activity. It is also important to note that despite examining several assessments within persons, the sample size was restricted to 29. Furthermore, responses to the questionnaires may have been biased toward the socially desirable response as the course's staff administered them, and another active group could have controlled for these effects. Consequently, it is recommended to conduct future studies with larger samples and a well-designed and controlled trial, in order to achieve more conclusive findings. Another limitation is that, while all the participants attended the whole course with a comparable (coherent) level of commitment to the practices (including the retreats), we did not verify their course-related activity and practices at home, and therefore, we have no way to check whether they actually did the practice activities at home as suggested during the modules.

Possible new directions of exploration of this study concern the age range of the participants, which, in our case, was limited to middle-aged individuals (39–66), and therefore, the effects on younger or older individuals remain currently unexplored. Another interesting direction would be to conduct follow-up measurements to assess the stability of the longitudinal effects months or years after the end of the program. Finally, while well-being and happiness are individual and subjective narratives of one's life as good and happy ( Bauer et al., 2008 ), and therefore self-assessments through questionnaires are a valid and common tool of investigation, in interventions such as The Art of Happiness , it would be appropriate to also explore individual differences, more objective psychophysiological effects, as well as cultural and social aspects influencing the inner model of happiness.

Despite these methodological limitations and still unexplored directions of research, the results described here suggest that The Art of Happiness may be a promising program for fostering well-being in individuals, improving mental health and psychological functioning. Longitudinal integrated contemplative programs with retreats offer a unique opportunity for the intensive development of the inner attitudes related to the capacity to be happy, reducing mental health symptoms and improving a more stable eudemonic well-being in healthy adults.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, Nicola De Pisapia, upon reasonable request.

Ethics Statement

The studies involving human participants were reviewed and approved by Ethics Committee of the Sapienza University of Rome. The participants provided their written informed consent to participate in this study.

Author Contributions

ND, CM, and AR designed the study. ND, CM, LC, and AR collected the data. CR analyzed the data. CR and ND wrote the original draft. All authors edited and reviewed the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We thank the Institute Lama Tzong Khapa (Pomaia, Italy) for the support in various phases of this experiment. We also wish to express our gratitude to the reviewers for their thoughtful comments and efforts toward improving the manuscript.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2021.600982/full#supplementary-material

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Keywords: meditation, wisdom, happiness, well–being, mindfulness

Citation: Rastelli C, Calabrese L, Miller C, Raffone A and De Pisapia N (2021) The Art of Happiness: An Explorative Study of a Contemplative Program for Subjective Well-Being. Front. Psychol. 12:600982. doi: 10.3389/fpsyg.2021.600982

Received: 31 August 2020; Accepted: 11 January 2021; Published: 11 February 2021.

Reviewed by:

Copyright © 2021 Rastelli, Calabrese, Miller, Raffone and De Pisapia. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Nicola De Pisapia, nicola.depisapia@unitn.it

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Happiness among higher education academicians: a demographic analysis

Rajagiri Management Journal

ISSN : 0972-9968

Article publication date: 20 March 2020

Issue publication date: 29 June 2020

To deal with highly energetic younger generation patiently, need academicians who can spread happiness while teaching/mentoring are needed. This is possible when an academician himself is a happy person. This paper aims to explore the factors that generate happiness among academicians, studies the impact of demographic variables on academicians’ happiness and examines the relationship between academicians’ happiness and their performance.

Design/methodology/approach

Convenience purposive sampling method was used to obtain data through self-administered survey questionnaire based on a five-point Likert scale, delineating the research purpose and assurance of confidentiality. For data analysis, statistical techniques like mean, percentage method, Levene’s test, t -test and analysis of variance were used. To study the relationship between performance and happiness, the attitude, motivation and outcome theory was applied and happiness index was developed.

After analyzing the various factors impacting academicians’ happiness, this study found that except for work–life balance, research activities and working environment, all other factors are available to academicians according to their ranked importance assigned to them. This study also obtained a happiness index using matrix and has developed an equation which can be applied to find out the relationship between happiness and performance in future.

Research limitations/implications

This study has certain limitations, first, this study has been conducted on academicians working in higher education institutes situated in Delhi/NCR and thus entails a specific socio-cultural environment that may limit the potential level of generalization.

Practical implications

The results of this research might help institutes/higher education bodies to make rules and policies which may further augment academicians’ happiness to accomplish their desired goals.

Social implications

An academician who is happy, satisfied and motivated can easily deal with today's enthusiastic younger generation and can spread happiness amongst them. so it is very much necessary for an academician to be happy and energetic all the time.

Originality/value

This study found the factors impacting higher education academicians’ happiness and its impact on their teaching performance.

• Higher education
• Negative emotions
• Academicians' performance
• Happiness index
• Happiness quotient
• Workplace happiness

Arora, R.G. (2020), "Happiness among higher education academicians: a demographic analysis", Rajagiri Management Journal , Vol. 14 No. 1, pp. 3-17. https://doi.org/10.1108/RAMJ-11-2019-0024

Emerald Publishing Limited

Copyright © 2020, Ritu Gandhi Arora.

Published in Rajagiri Management Journal . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

Longman’s dictionary (2005, p. 634) defines happiness as “state of being happy”, means a feeling of gratification, i.e. something is fine or correct, as being satisfied with something, not apprehensive or about being fortunate and doing well. Happiness is generally confused with a form of mood or emotion or satisfaction; also, both these terms are used interchangeably by many authors. Happiness has been termed as positive emotions by various psychologists. Workplace happiness is the result of strategies, principles, rules and regulations made by the top management. It is a general notion amongst employees that if they are successful at their job and completing all their targets well in time, they are happy. But today, the scenario has been reversed. It is important to be happy, which will then help people become a success. There are enormous changes coming in the work environment. Long-established systems, policies, rules and strategies might not be apt for today’s generation. For this generation, the meaning of work and work style has also changed. Old customs need revalidation, and new approaches require fast adaptation. It is apparent of one becoming irritating and annoyed after a stretched and chaotic schedule, but this may not even happen if one finds his/her work interesting enough. Getting engaged in work results in high productivity and will automatically generate interest only when employees are feeling happy at work place. Being happy is the key to productivity ( Djoen and Hewagamage, 2016 ), and it has considerable relationship with performance ( Michael, 1989 ). Employers also look forward to a high-performing employee who in turn gives high productivity, to attain organizational goals. To enhance employee productivity, management adopts various strategies like rewards/incentives, direction communication with staff members, top management supports, employee involvement in decision making and so on.

Conceptualization

Happiness is subjective, i.e. a feeling of well-being experienced by an individual, specially featured by the presence of affirmative emotions and the nonappearance of negative emotions. It may be distinct as the experience of recurrent positive effect, infrequent negative effect and, on the whole, a sense of satisfaction with life ( McBride, 2010 ). Happiness at work is closely correlated with greater performance and productivity as well as greater energy, better reviews, faster promotion, higher income, better health and long life. If taken as a whole, the idea of happiness is how much you like what you have or do. Even if two persons have everything equal, they may differ in their happiness, as it depends on how much you actually require, i.e. your expectations may differ.

In an academician’s career, his/her happiness not only depends upon job satisfaction, students' results and feedback. Government systems, its pay policies and organizational hierarchies also plays a major role. Academicians work in an altogether different environment, i.e. they deal with the younger generation in classrooms, matured individuals and learned faculty outside the classrooms and knowledgeable entrepreneurs to understand industry requirements.

Even though many studies available on the relationship between happiness and productivity, performance, stress among employees, etc. that concentrate on many industries, e.g. construction, Information Technology (IT), Information Technology Enabled Services (ITeS), manufacturing, textile, telecom, etc. but very few studies are available as far as academicians’ happiness is concerned. Among academicians also, the higher education faculty plays the crucial role in shaping the personality of students from unrefined human product to refined saleable product to be further consumed by industry and later by the economy. Their low happiness level influences their knowledge sharing in the classrooms and ultimate sufferers are none other than students (Ministry of Human Resource Development (MHRD) Survey, 2015-16). So, to enhance their performance, keeping them happy is exceptionally important across the education sector.

This study mainly focuses on finding out the various factors which impact their happiness at workplace. The results of this research might help institutes/higher education bodies to make rules and policies which may further augment academicians’ happiness to accomplish their desired goals.

Literature review

Ford et al. (2003) argued that happiness involves activities that convey a sense of pleasantness, happiness and positive well-being, that not only make working satisfied but also fun. In psychology, happiness is a relatively positive perception about self, but definitely not total absence of negative emotions ( Diener and Satvik, 1991 ). Happiness at workplace has positive effects on performance. To make employees happy, companies must decide the factors that contribute to their happiness and pleasure at workplace. Workplace happiness and relationship between employees (individual or group) are, therefore, positively related to each other. Frey and Stulzer (2000) examined three factors of happiness, i.e. personality and demographic factors (work, income, community, value, religion, family, experience, education, gender and age), micro- and macro-economic factors (per capita income, employment, inflation) and third is institutional factors like democracy and federalism. Whereas, Graham et al. (2004) mentioned that happiness is subject to various changes and fluctuations; it is a part of our nature, inherent in us by our parents through genes.

Factors affecting happiness at workplace among academicians

Hill (1986) has reported empirical support for extrinsic factors such as salary, administrative work and fringe benefits as far as happiness among faculty is concerned, but he also supported research and teaching as intrinsic happiness factors. Lacy and Sheehan (1997) , contended work environment, organization’s atmosphere, relationship with colleagues as predictors of happiness among academicians. Leung et al. (2000) observed further that acknowledgment, management policies and monetary sufficiency are the predictors of job happiness among academicians. Mushtaq and Sajid (2013) in their study found that classroom environment makes academicians happy. If their students are happy, they do not even feel the work load stress. Jennifer (1996) discussed the impact of financial rewards, classroom teaching culture, role diversity, autonomy and organizational structure on the academician’s happiness at work. Further in this, Farren and Nelson (1999) underlined that the employees’ feel connected with those organizations which carry out mixture of staff development program compared with those who do not. Since long, researchers have also maintained that variety of facilities (monetary/non-monetary) have positive effects on employees’ attitudes ( Simons et al. , 2007 , Butter, Lowe, 2010). Empirical research done in Lithuania depicts that employee-oriented practices always have a significant and positive relation with employee motivation as well as their happiness also affects employee turnover intentions.

Academic institutions transmit knowledge and develop students; their poor performance or low morale can influence the knowledge sharing, and the ultimate sufferers are the future generations. At apex level, the Indian higher education industry has number of central, state, deemed and private universities ( All India Survey on Higher Education 2016‐17, 2017 ). This industry is either short of manpower or the quality of faculty is very poor in terms of communication skills, subject expertise, industry academia interface, etc. This requires the severe need for enhancing the attractiveness of teaching as a profession as well as motivator to select this profession by choice not by compulsion amongst the young generation.

Objectives of the study

find out factors influencing happiness of academicians at various institutional levels;

explore the difference in happiness level of academicians working at different hierarchical levels in terms of demographic variables like age, gender and designation; and

use the differences for framing a mathematical model to study the relationship between academicians’ happiness and their performance using the attitude, motivation and outcome (AMO) theory.

Workplace happiness factors significantly differ among demographic variables like age, education and designation.

Workplace happiness factors do not significantly differ among demographic variables like age, education and designation.

Research methodology

The research study was conducted on academicians working in various universities (government, private and deemed) and colleges (self-financing or aided) located in and around Delhi/NCR. Convenience purposive sampling method was used to obtain data through self-administered survey questionnaire based on a five-point Likert scale, delineating the research purpose and assurance of confidentiality. Respondents were given the liberty of not to give their identifiable information to maintain the anonymity of the responses. The questionnaire included the instruments related to top management support, job satisfaction and work culture. Of 350, 336 duly filled questionnaires were received back via mail or in person. A total of 21 of 336 returned questionnaires were found to be invalid, so, in total, 315 responses were used for further analysis. The study was conducted from January 2018 to February 2019.

For data analysis, statistical techniques like factor analysis, mean, rank/percentage method, Levene’s test, t -test and analysis of variance (ANOVA) were used. Levene’s test was used to test the equality of variances for a variable calculated for two or more groups (Levene, 1960).

Reliability analysis

Table I represents the reliability coefficient of all scales used in this study. The reliability of the questionnaire was checked through Cronbach’s alpha which is used to estimate the reliability of a psychometric test. Closer the Cronbach’s alpha coefficient is to 1.0, the greater the internal consistency of the items in the scale (Gliem and Gliem, 2003). The results of the test show that the items are reliable, i.e. 0.882. The Kaiser–Meyer–Oklin (KMO) value for these variables was 0.859, indicating that the sample size was adequate for applying factor analysis (Field, 2005).

Results and discussion

The sample comprises all categories of academicians including assistant professors, associate professors and professors having minimum qualification required for the appointment on the concerned post. The sample was selected keeping in mind the faculty/student (1:2:3) ratio decided by UGC/AICTE also to provide due and adequate representation to various other variables like age, sex, gender, nature of organization, job nature and department. The various classifications of samples are duly represented in Table II .

Exploratory factor analysis

The variables with loadings of at least 0.5 (Hair et al. , 2006) were included in the analysis. For factor extraction, principal component method was used. Eight factors were obtained and named according to the variables included in them. These factors with their names and respective loadings are shown in Table III .

To find out the factors affecting academician’s happiness level in an organization, factor analysis was applied and eight factors were obtained as a result of the exploratory factor analysis, namely, research activities (F1), working environment (F2), fringe benefits (F3), personal growth (F4), job security (F5), salary (F6), work–life balance (F7) and involvement in social endeavors (F8). Mean and standard deviation (SD) of the various happiness factors thus obtained affecting happiness at workplace and their rankings are shown in Table IV .

Table IV shows that academicians want F4 ( x ̄ = 4.32) through a well-structured organization chart/defined hierarchy; they expect an institute to define their career path clearly at the time of joining or through a well-defined individual career plan. Also, because of government emphasis and increasing awareness among public for social causes, academicians have given importance to institutional F8 to serve societies and their involvement in same ( x ̄ = 4.21).

To establish the difference between the happiness factors and various demographic variables, ANOVA and t -test have been applied. Further, the significant relationship between the groups within a demographic characteristic has been tested by applying the post hoc test.

Gender-wise comparison of factors affecting academicians’ happiness at workplace

Academicians may have different views regarding happiness factors. To find out whether there is any significant difference between the mean score of male and female academicians, t -test has been applied ( Table V ). Highest mean value for F7 for both females ( x ̄ = 4.35) and males ( x ̄ = 4.27) depicts that both men and women want to maintain equity in their professional and personal life. They give equal priority to enjoyment and work. For both, F6 is the second important factor which makes them happy. Whereas, in case of female academicians, their involvement in social awareness programs gives them happiness, and male academicians feel happy when they are more involved in what and why questions related to various issues at social and professional front, i.e. their involvement in F1.

Further, the results show that there is a significant difference between male and female academicians in the influence of F3, F5 and F8 on their happiness.

Null hypothesis is hence rejected, as there is a significant difference between male and female respondents regarding various factors affecting their happiness while working and performing in an institution.

Age-wise comparison of factors affecting happiness

Age of an academician also came out as an important factor, which determines happiness quotient of academicians. Academicians under 35 years of age rate F7 and F2 at work place as more important than their F6 and growth prospects in the college/institute as one of the important reasons to be happy. Whereas, academicians above 35 years of age feel happy when they are involved in F1, F6 and are able to maintain F7. They feel happy when an institute offers them competitive pay package and also provides them sufficient time and facilities to balance their work and life ( Table VI ).

The comparison of factors between different age groups of respondents regarding factors impacting their happiness at workplace differs significantly except on two factors, i.e. F7 and F8. Study clearly stated that because of the difference in age, employee priorities also change; at one point of time, he/she gives more preference to F6 and at another point of time he/she is more in favor of research and CSR activities. To be happy at workplace, academicians need regular feedback and appropriate appraisals. Hence, the null hypothesis is rejected, and alternate hypothesis accepted for these factors.

The post hoc test results ( Table VI ) reveal that the difference is significant among the different age group for six factors (except F7 and F8).

Designation-wise comparison of factors affecting happiness

Table VII shows that assistant professors feel happy when they have been provided cordial Work Environment (F2) in an institute ( x ̄ = 4.27) through which they can maintain coordination between their family and job F2 ( x ̄ = 4.22). Teaching is known to be a profession which needs dedication and hard work not only for self but also for society. So, faculty needs to be calm and cool while dealing with young generation of 20-25 years of age.

Associate professors gives importance to factors which ensures their F5 ( x ̄ = 4.47) along with F1 ( x ̄ = 4.40) and F7 ( x ̄ = 4.40), and same is in the case of professors. They also want to be involved in more research projects ( x ̄ = 4.45) sponsored/funded by UGC or companies, respectively. But simultaneously, they are also of a viewpoint that maintaining work–life and good F6 package is equally important because of family responsibilities and presence of growing/teenage kids at home.

As per the results shown in Table VII , hypothesis H0 that designation of faculty member significantly influences workplace happiness among academicians is accepted in case of five major factors, namely, F1, F2, F3, F4 and F5. The post hoc results also state that this difference is significant in case of these five factors only.

Mathematical model and equation to draw the relationship between academicians’ performance and happiness using the AMO theory

After exploring the factors influencing higher education academicians’ happiness level, the interaction of extracted factors has been used to draw a matrix.

In this study, three matrices are used to represent the relationship among the factors affecting happiness at the three designations: assistant professor, associate professor and professors, because of difference in factors influencing happiness at the three hierarchical levels; so, to determine the numerical happiness index, the permanence of the matrices is evaluated. The permanent is similar to determinant of matrix but with all signs positive, e.g.: perm ( a b c d e f g h i ) = a e i + b f g + c d h + c e g + b d i + a f h .

The permanent of assistant professor matrix: perm ( M A P ) = ( ( ( D 4 F 6 G 2 + B 2 D 4 F 6 )  H 7 + ( D 4 F 7 G 2 +   B 2 D 4 F 7 ) H 6 +   B 2 D 4 F 6 G 7 )   I 9 + ( ( D 4 F 9 G 2 + B 2 D 4 )   H 6 + B 2 D 4 F 6 )   I 7 + ( B 11 D 4 F 6 G 9 H 7 +   ( D 4 F 7 G 9 +   D 4 G 7 )  H 6 )   I 2 )   J 10 K 8 + ( ( B 11 D 4 F 6 G 2 + B 2 D 4 F 6 G 11 )   H 8 I 9 +   B 11 D 4 F 6 G 9 H 8 I 2 )   J 10 K 7 + ( B 11 D 4 F 6 G 7 H 8 I 9 + B 11 D 4 F 6 G 9 H 8 I 7 ) J 10 K 2 +   ( B 2 D 4 F 6 G 7 H 8 I 9 + B 2 D 4 F 6 G 9 H 8 I 7 )   J 10 K 11 +   ( ( ( B 11 D 4 F 6 G 2 +   B 2 D 4 F 6 G 11 )   H 8 I 7 + B 11 D 4 F 6 G 7 H 8 I 2 )   J 9 + ( ( ( B 11 D 4 F 6 G 2 +   B 2 D 4 F 6 G 11 )   H 7 + ( B 11 D 4 F 7 G 2 + B 2 D 4 F 7 G 11 )   H 6 +   B 2 D 4 F 6 G 7 H 11 )   I 9 +   ( ( B 11 D 4 F 9 G 2 +   B 2 D 4 F 9 G 11 )   H 6 +   B 2 D 4 F 6 G 9 H 11 )   I 7 + ( B 11 D 4 F 6 G 9 H 7 +   ( B 11 D 4 F 7 G 9 +   B 11 D 4 F 9 G 7 )   H 6 )   I 2 )   J 8 +   ( ( B 11 D 4 F 7 G 2 +   B 2 D 4 F 7 G 11 )   H 8 I 9 +   ( B 11 D 4 F 9 G 2 + B 2 D 4 F 9 G 11 )   H 8 I 7 +   ( B 11 D 4 F 7 G 9 +   B 11 D 4 F 9 G 7 )   H 8 I 2 )   J 6 +   ( B 11 D 4 F 6 G 7 H 8 I 9 +   B 11 D 4 F 6 G 9 H 8 I 7 )   J 2 )   K 10

The permanent of associate professor matrix: perm   ( M ASOP ) =   ( ( ( A 1 D 4 E 5 F 12 +   ( A 12 D 4 E 5 +   A 5 D 4 E 12 )   F 1 )   G 11 H 6 +   ( A 1 D 5 E 12 F 6 +   A 6 D 5 E 12 F 1 )   G 11 H 4 +   ( A 1 D 4 E 5 F 6 +   A 6 D 4 E 5 F 1 )   G 11 H 12 +   ( A 1 D 4 E 5 F 6 +   A 6 D 4 E 5 F 1 )   G 12 H 11 +   ( ( A 12 D 4 E 5 +   A 5 D 4 E 12 ) F 6 +   A 6 D 4 E 5 F 12 )   G 11 H 1 )   K 7 +   ( ( A 1 D 4 E 5 F 6 +   A 6 D 4 E 5 F 1 )   G 12 H 7 +   ( ( A 1 D 4 E 5 F 12 +   ( A 12 D 4 E 5 +   A 5 D 4 E 12 )   F 1 )   G 7 +   A 1 D 4 E 5 F 7 G 12 )   H 6 +   ( A 1 D 5 E 12 F 6 +   A 6 D 5 E 12 F 1 )   G 7 H 4 +   ( A 1 D 4 E 5 F 6 +   A 6 D 4 E 5 F 1 )   G 7 H 12 +   ( ( ( A 12 D 4 E 5 +   A 5 D 4 E 12 )   F 6 +   A 6 D 4 E 5 F 12 )   G 7 +   A 6 D 4 E 5 F 7 G 12 )   H 1 )   K 11 )   L 8 +   ( ( ( A 1 D 4 E 5 F 12 + ( A 12 D 4 E 5 +   A 5 D 4 E 12 )   F 1 )   G 11 H 6 +   ( A 1 D 5 E 12 F 6 +   A 6 D 5 E 12 F 1 )   G 11 H 4 +   ( A 1 D 4 E 5 F 6 +   A 6 D 4 E 5 F 1 )   G 11 H 12 +   ( A 1 D 4 E 5 F 6 +   A 6 D 4 E 5 F 1 )   G 12 H 11 +   ( ( A 12 D 4 E 5 +   A 5 D 4 E 12 )   F 6 +   A 6 D 4 E 5 F 12 )   G 11 H 1 )   K 8 +   ( ( A 1 D 4 E 5 F 6 +   A 6 D 4 E 5 F 1 )   G 12 H 8 +   A 5 D 4 E 8 F 1 G 12 H 6 +   ( A 1 D 5 E 8 F 6 +   A 6 D 5 E 8 F 1 )   G 12 H 4 +   A 5 D 4 E 8 F 6 G 12 H 1 )   K 11 )   L 7 +   ( ( ( A 1 D 4 E 5 F 6 +   A 6 D 4 E 5 F 1 )   G 11 H 7 +   A 1 D 4 E 5 F 7 G 11 H 6 +   ( A 1 D 4 E 5 F 6 +   A 6 D 4 E 5 F 1 )   G 7 H 11 +   A 6 D 4 E 5 F 7 G 11 H 1 )   K 8 +   ( ( A 1 D 4 E 5 F 6 +   A 6 D 4 E 5 F 1 )   G 11 H 8 +   A 5 D 4 E 8 F 1 G 11 H 6 +   ( A 1 D 5 E 8 F 6 + A 6 D 5 E 8 F 1 )   G 11 H 4 +   A 5 D 4 E 8 F 6 G 11 H 1 )   K 7 +   ( ( A 1 D 4 E 5 F 6 +   A 6 D 4 E 5 F 1 )   G 7 H 8 +   A 5 D 4 E 8 F 1 G 7 H 6 + ( A 1 D 5 E 8 F 6 +   A 6 D 5 E 8 F 1 )   G 7 H 4 +   A 5 D 4 E 8 F 6 G 7 H 1 )   K 11 )   L 12 +   ( ( ( ( A 12 D 4 E 5 +   A 5 D 4 E 12 )   F 6 +   A 6 D 4 E 5 F 12 )   G 11 H 7 +   ( A 12 D 4 E 5 +   A 5 D 4 E 12 )   F 7 G 11 H 6 +   A 6 D 5 E 12 F 7 G 11 H 4 +   A 6 D 4 E 5 F 7 G 11 H 12 +   ( ( ( A 12 D 4 E 5 +   A 5 D 4 E 12 )   F 6 +   A 6 D 4 E 5 F 12 )   G 7 +   A 6 D 4 E 5 F 7 G 12 )   H 11 )   K 8 +   ( ( ( A 12 D 4 E 5 +   A 5 D 4 E 12 )   F 6 +   A 6 D 4 E 5 F 12 ) G 11 H 8 +   A 5 D 4 E 8 F 12 G 11 H 6 +   ( A 12 D 5 E 8 F 6 +   A 6 D 5 E 8 F 12 )   G 11 H 4 +   A 5 D 4 E 8 F 6 G 11 H 12 +   A 5 D 4 E 8 F 6 G 12 H 11 )   K 7 +   ( ( ( ( A 12 D 4 E 5 +   A 5 D 4 E 12 )   F 6 +   A 6 D 4 E 5 F 12 )   G 7 +   A 6 D 4 E 5 F 7 G 12 )   H 8 +   A 5 D 4 E 8 F 6 G 12 H 7 + ( A 5 D 4 E 8 F 12 G 7 +   A 5 D 4 E 8 F 7 G 12 )   H 6 +   ( ( A 12 D 5 E 8 F 6 +   A 6 D 5 E 8 F 12 )   G 7 +   A 6 D 5 E 8 F 7 G 12 )   H 4 +   A 5 D 4 E 8 F 6 G 7 H 12 )   K 11 )   L 1

The permanent of professors matrix is: p e r m a (   M P ) =   ( ( A 1 F 6 +   A 6 F 1 )   G 12 H 7 +   ( ( A 1 F 12 +   A 12 F 1 )   G 7 +   A 1 F 7 G 12 ]   H 6   +   ( A 1 F 6 +   A 6 F 1 )   G 7 H 12 +   ( ( A 12 F 6 +   A 6 F 12 )   G 7 +   A 6 F 7 G 12 ) ) H 1 ) L 8 +   ( A 1 F 6 +   A 6 F 1 )   G 12 H 8 L 7 +   ( A 1 F 6 +   A 6 F 1 )   G 7 H 8 L 12 +   ( ( A 12 F 6 +   A 6 F 12 )   G 7 +   A 6 F 7 G 12 )   H 8 L 1

The permanence of this matrix has been used to quantify the qualitative happiness factors. The happiness index thus obtained through the matrix has been related to the performance of the academicians. Thus, the factors of happiness are converted to a numerical value through which the degree of performance can be ascertained. So, this matrix helped to quantify the qualitative factors of happiness. According to Davidoff (1987), individual performance is generally determined by three factors, namely, ability – the capability to do the job; work environment – the tools, materials and information needed to do the job; and motivation – the desire to do the job happily and readily.

In this paper, matrix is used to show the relationship between various happiness factors affecting three different levels taken for study, i.e. assistant professor, associate professor and professors. The factors affecting different academicians working at different levels are related to each other. Through GTA, i.e. through digraph, matrix and permanent function, the happiness index of assistant professor (perma H AS ), associate professor (perma H ASOP ) and of professors (perma H P ) is obtained. Through this, the happiness index of academicians (HI A ) can be given as: H I A =   p e r m a   H A S +   p e r m a   H A S O P +   p e r m a   H P

The happiness index, thus, obtained is linked to the academician’s performance in the classroom as well in the institute.

The ability Ai to perform has to be understood in a broader sense. It includes an employee’s knowledge, skills and abilities. This relationship is based on the AMO theory where (Pi) is the performance of an individual, (i) is function (f) of his or her ability (Ai) to perform, his or her happiness/willingness to perform happily (Hi) and the opportunity to perform in the job is Oi (Boxall and Purcell, 2011): P e r f o r m a n c e   P i   =   A b i l i t y   o f   a n   i n d i v i d u a l   t o   p e r f o r m   A i × H a p p i n e s s   H i × O p p o r t u n i t y   t o   p e r f o r m   O i

The derived happiness index obtained can further be used to measure the performance of an individual and, ultimately, the performance of an organization as a whole. The happiness index can be used in the AMO theory as follows:

Performance of an organization = Sum total of performance of employees of the organization. As per the results of present study, the performance of an academic institution can be measured as: P A I =   H I A     ×   N   A b i l i t y   o f   A c a d e m i c i a n   ×   O p p o r t u n i t y   p r o v i d e d   t o   A c a d e m i c i a n

Where, P AI is the performance of an academic institution and N is the number of academicians in the institution.

Conclusion and suggestions

The results of the study clearly show that most of the academicians irrespective of their age, experience and designation ranked F7 and F2 of an institute or college as most important happiness factors. The reason for ranking these factors as most important could be because of high family expectations along with student’s expectations from their faculty. Because of the increasing use of ICT tools in teaching and training, students and faculty involvement has become of 24/7, which might have become troublesome for faculty members. In comparison to government universities/aided colleges, private college faculties need more upgradation with the latest technological innovations; they have more work pressures, less holidays and no time barrier. Consequently, academicians do not find much time for their families and leisure activities. So, the management should provide them proper facilities, holidays to help them to lead a balanced life. When faculty stays for long hours in the campus, they should be compensated properly so that they should not feel that their jobs are taking a toll on them. Some faculty members look for more sponsored research work to be happy, so whenever management gets a sponsored project, interested faculty members should be given the opportunity to take that project further.

There are only few faculty members who have given importance to F6; this is somehow in contradiction to the earlier literature, where most of the faculty members specifically in the age group of 25-30 years and at the assistant professor level, have ranked F6 as the most important happiness factor.

The study analyzed the various factors which impact academicians’ happiness and found that except for F7, F1 and F2, all other factors are available to academicians according to their ranked importance assigned to them by respondents. This study also obtained a happiness index using matrix and has developed an equation which can be applied to find out the relationship between happiness and performance. This study contributes to the body of literature by applying a customized set of happiness factors on understudied but important respondents, i.e. higher education academicians.

Implications of the study and scope for further research

This study quantified the qualitative aspects by converting the happiness factors thus obtained in to numerical value through which the degree of performance can be ascertained. So, the research findings can help the management to develop effective strategies for keeping academicians happy, thus leading to quality teaching. The results of the study can be further used to find the ability index, opportunity index of the employees and, ultimately, the entire quantification of performance can be done.

Limitations

This study has certain limitations, which should be kept in mind while applying the findings. First, this study has been conducted on academicians working in higher education institutes situated in Delhi/NCR, and thus entails a specific socio-cultural environment that may limit the potential level of generalization.

Reliability tests

Demographic profile of respondents

Source: Primary Data; F1: Research activities, F2: Working environment, F3: Fringe benefits, F4: Personal growth, F5: Job security, F6: Salary, F7: Work–life balance, F8: Social endeavors; * indicates significance at 0.00 level

All India Survey on Higher Education 2016‐17 ( 2017 ), Ministry of Human Resource Development Department of Higher Education , Govt. of India , New Delhi .

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Graham , C.E. , and et al. ( 2004 ), “ Does happiness pay: an exploration based on panel data ”, Journal of Economic Behavior and Organization , Vol. 55 , pp. 319 - 342 .

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Mushtaq , A. and Sajid , I.S. ( 2013 ), “ Factors responsible for high and low happiness level of university academicians ”, International Journal of Science and Research , Vol. 2 No. 2 , pp. 21 - 34 .

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Further reading

Andrew , S.S. ( 2011 ). “ S.M.I.L.E.S.: the differentiating quotient for happiness at work ”, available at: www.happiestminds.com

Annie , M. ( 2014 ), “ Being happy at work matters ”, Harvard Business Review , pp. 23 - 37 .

Atkinson , C. and Hall , L. ( 2011 ), “ Flexible working and happiness in the NHS ”, Employee Relations , Vol. 33 No. 2 , pp. 88 - 105 .

Chun , R. and Davies , G. ( 2009 ), “ Employee happiness is not enough to satisfy customers ”, Harvard Business Review , pp. 65 - 78 .

Dutton , V.M. and Edmunds , L.D. ( 2007 ), “ A model of workplace happiness ”, Selection and Development Review , Vol. 23 No. 1 , pp. 14 - 23 .

Gavin , J.H. and Mason , R.O. ( 2004 ), “ The virtuous organization: the value of happiness in the workplace ”, Organizational Dynamics , Vol. 33 No. 4 , pp. 379 - 392 .

Pogue , J. and Lucken , E. ( 2014 ), “ Happiness (or is it really purpose?) at work ”, available at: www.gensleron.com/work/2014/6/5/happiness-or-is-it-really-purpose-at-work.html

Rego , A. and Cunha , M.P. ( 2009 ), “ How individualism and collectivism orientations predict happiness in a collectivistic context? ”, Journal of Happiness Studies , Vol. 10 , pp. 19 - 35 .

Suojanen , I. ( 2012 ), “ Work for your happiness: theoretical and empirical study defining and measuring happiness at work ”, Thesis, University of Turku .

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Open Access

Peer-reviewed

Research Article

The Nurses’ Well-Being Index and Factors Influencing This Index among Nurses in Central China: A Cross-Sectional Study

Contributed equally to this work with: Runtang Meng, Yi Luo

Affiliation Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, Hubei, 430071, P. R. China

Affiliation School of Nursing, Ningbo College of Health Sciences, Ningbo, Zhejiang, 315100, P. R. China

Affiliation Center of Health Administration and Development Studies, Hubei University of Medicine, Shiyan, Hubei, 442000, P. R. China

Affiliations Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, Hubei, 430071, P. R. China, Global Health Institute, Wuhan University, Wuhan, Hubei, 430072, P. R. China

* E-mail: [email protected]

• Runtang Meng, 
• Yi Luo, 
• Bing Liu, 
• Ying Hu, 
• Chuanhua Yu

• Published: December 17, 2015
• https://doi.org/10.1371/journal.pone.0144414
• Reader Comments

Backgrounds/Objectives

A discussion and analysis of factors that contribute to nurses’ happiness index can be useful in developing effective interventions to improve nurses’ enthusiasm, sense of honor and pride and to improve the efficiency and quality of medical services.

In this study, 206 registered nurses at the 2011 annual encounter for 12 Hanchuan hospitals completed a questionnaire survey that covered three aspects of the well-being index and thus served as a comprehensive well-being and general information tool.

Based on their index score, the nurses’ overall happiness level was moderate. The dimensions of the happiness index are listed in descending order of their contribution to the nurses’ comprehensive happiness levels: health concerns, friendly relationships, self-worth, altruism, vitality, positive emotions, personality development, life satisfaction and negative emotions. Four variables (positive emotion, life satisfaction, negative emotions, and friendly relationships) jointly explained 47.80% of the total variance of the happiness index; positive emotions had the greatest impact on the happiness index.

Conclusions

Appropriate nursing interventions can improve nurses’ happiness index scores, thereby increasing nurses’ motivation and promoting the development of their nursing practice.

Citation: Meng R, Luo Y, Liu B, Hu Y, Yu C (2015) The Nurses’ Well-Being Index and Factors Influencing This Index among Nurses in Central China: A Cross-Sectional Study. PLoS ONE 10(12): e0144414. https://doi.org/10.1371/journal.pone.0144414

Editor: Sari Helena Räisänen, University of Helsinki, FINLAND

Received: May 12, 2015; Accepted: October 15, 2015; Published: December 17, 2015

Copyright: © 2015 Meng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: Data are available from the social survey Institutional Data Access / Ethics Committee for researchers who meet the criteria for access to confidential data. Data are from the survey study whose authors may be contacted at Meng Runtang.

Funding: This work was supported by the National Natural Science Foundation of China (Grant No. 81273179) and the scientific research project for university student of Hubei University of Medicine (Grant No. 2011XS16), and as a summer social practice project of the Institute of Medicine and Nursing in 2011.

Competing interests: The authors have declared that no competing interests exist.

Introduction

China is at the forefront of social concerns about medical work, and medical professionals have witnessed tremendous changes in their work environment in recent years. Studies have found that high levels of occupational stress can lead to feelings of self-doubt, irritability, and sleep disorders [ 1 , 2 ]. Effort-reward, work-life imbalance, interpersonal conflict, general stress and burnout and can influence nurses’ subjective well-being [ 1 – 5 ]. Nurses are a special population because they experience high levels of stress in their everyday work [ 6 , 7 ].

Happiness reflects the core indicators of subjective quality of life, the value that reflects a person’s happiness within a given period [ 8 ]. The nurses’ happiness index measures how happy nurses are with providing care; nurses with higher happiness index values have greater professional initiative [ 9 , 10 ]. Employee performance-management features have different impacts on different aspects of well-being; and emotional demands from the nursing profession can act as challenges which promote motivation and well-being [ 11 , 12 ].Therefore, the happiness index of nurses directly affects the efficiency and quality of medical services; consequently, understanding and improving nurses’ quality of life is important. In this paper, 206 registered nurses from 12 hospitals in Hanchuan completed a questionnaire survey. Based on the results of the survey, we analyzed the impact of factors that affect nurses’ happiness index values with the aim of using that information to develop effective interventions. The survey results are reported below.

From June to July 2011, 206 registered nurses from 12 hospitals of various levels in Hanchuan City, Hubei Province, were recruited using a random sampling method. A total of 220 questionnaires were sent out, and 160 questionnaires were distributed by nurses in three secondary hospitals (The People's Hospital, a Chinese medicine hospital, and a maternal and child health care hospital); 60 were distributed by nurses in nine primary care hospitals (8 township health centers and 1 community health service center). A total of 206 valid questionnaires were completed, and the questionnaire return rate was 93.64%. The subjects’ inclusion criteria were as follows: currently working, had obtained a medical practice certificate, and had no mental illness or disturbance of consciousness when the study was conducted. The exclusion criteria were as follows: re-employment after retirement, retired nurses and nurses who were engaged in advanced studies.

Survey Tools

The general information questionnaire for nurses included hospital level (primary, secondary, or tertiary), age, length of service, work department, level of education, job title, employment status, marital status, and monthly income. The Multiple Happiness Questionnaire (MHQ) [ 13 ] included nine dimensions: life satisfaction(5 items), positive emotions (6 items), negative emotions(6 items), life vitality(6 items), health concern(5 items), altruism behavior(5 items), self-worth(5 items), friendly relationship(3 items) and personal growth(9 items). These dimensions were measured with 50 items using a Likert 7-level scale. For items A1-A38, the scale ranged from very uncomfortable to very comfortable, among them, entries A12 and A14, take the reverse scoring method; and for items B1-B12, the scale ranged from never to always; this study used a Likert 7 level score, and a reversed scale was used to score negative emotions. Higher scores indicated a stronger sense of happiness. A 9-level Likert scale was used to rate happiness (happiness index) from very unhappy to very happy: 1 indicated a low level of happiness, 3.67 indicated a moderate level of happiness, and 6.33 and higher indicated a high level of happiness. There are in this questionnaire, nine aspects belonging to psychological well-being and subjective well-being respectively. The 9 dimensions of the research on the MHQ were calculated respectively by Cronbach's coefficient, which is between 0.9056 and 0.6742, among them, the friendship between the highest (0.9056), the personality development dimension is lower (0.6742) [ 13 ]. In this study, the scale’s overall coefficient of internal consistency (Cronbach’s alpha value) was 0.941.

Survey Methods and Ethics Statement

To ensure the validity and consistency of the questionnaire survey, the trained researchers engaged in conversation and communication with the management staff of the participating hospitals and health service centers; they then explained to the respondents how to complete the questionnaire, providing identical instructions to each participant. Each respondent was given ten to thirty minutes to complete the form independently. Anonymity was maintained, and the forms were recycled. All collected data is summarized in S1 Table .

The Institutional Review Board of Wuhan University School of Medicine, China, approved the study protocol in S2 Table . This study followed the Helsinki Convention’s norms and later modifications as well as the uniform requirements for manuscripts submitted to biomedical journals. This team ensured that the data collection process to fully respect and protect personal privacy. Fill out the instructions of the questionnaire also have instructions, respondents (nurses) to fill in the questionnaire, on behalf of their informed consent, and acknowledged our questionnaire information is not registered. Their written consent to participate in this topic research.

Statistical Methods

We employed double parallel data entry with EpiData (version 3.1, Lauritsen JM & Bruus M, Odense, Denmark) and consistency testing using the Statistical Package for the Social Sciences software (version 18.0, SPSS, Inc., Chicago, IL, USA) to conduct a descriptive analysis. Pearson’s correlation analysis and multiple linear stepwise regression analysis were also employed. All tests were two-sided, and statistical significance was set at p <0.05.

General Information

A total of 206 female nurses were included in this survey; among them, 57 (27.7%) were working in primary hospitals, and 149 (72.3%) were working in secondary hospitals. Other participant characteristics are shown in Table 1 .

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https://doi.org/10.1371/journal.pone.0144414.t001

Comprehensive Sense of Happiness: Happiness Index Scores

The results showed that the nurses had a moderate level of happiness. As shown in Table 2 , according to the happiness index, the dimensions of comprehensive happiness are as follows, in descending order of importance: attention to health, friendly relationships, self-value, altruistic behavior, life vitality, positive emotions and personal growth, life satisfaction, and negative emotions.

https://doi.org/10.1371/journal.pone.0144414.t002

The Correlation between the Happiness Index and the Dimensions of the MHQ

As table 3 shows, the Pearson correlation analysis results indicated that except for negative emotions, all other dimensions of the MHQ were positively correlated with the happiness index (all p <0.001). Lower negative emotion scores indicate a higher happiness index; the opposite relationship applies for the other eight dimensions of the MHQ.

https://doi.org/10.1371/journal.pone.0144414.t003

Regression Analysis of the Relationship between the Happiness Index and Each Dimension of the MHQ

This study set the happiness index as the dependent variable and the nine dimensions of the MHQ as the independent variables. In a certain range, the random variable X (independent variable) is subject to normal distribution, and the random variable Y (dependent variable) is given. By tests of Normality, we can see that the happiness index (= Y) is subject to normal distribution; Kolmogorov-Smirnov P = 0.200>0.05. The dependent measure is normally distributed. Each individual observation is independent of each other. According to the standards α in ≤0.05 and α out ≥0.10, a multiple linear stepwise regression analysis of the relationship between the happiness index and each dimension of the MHQ was conducted. Table 4 shows that the well-being index was positively related with positive emotions, life satisfaction, and friendly relationships but was negatively correlated with negative emotions. As for the regression equation model, the multiple correlation coefficient was R = 0.692 and the determination coefficient was R 2 = 0.478, which indicates that the above four factors can explain 47.80% of the happiness index total variance, and among them, positive emotions had the highest impact on the happiness index.

https://doi.org/10.1371/journal.pone.0144414.t004

Regression Analysis of the Relationships between the Happiness Index and General Information

Using the happiness index as the dependent variable and general information as the independent variable according to standards α in ≤0.05 and α out ≥0.10, a multiple linear stepwise regression analysis between the happiness index and general information was conducted. Table 5 shows that the happiness index was positively correlated with the stress response and professional titles and negatively correlated with work pressure. Regarding the regression equation model, the multiple correlation coefficient was R = 0.267, and the determination coefficient was R 2 = 0.071, which indicates that stress coping styles, professional titles and work pressure can explain 7.10% of the happiness index total variance and among these factors, stress coping styles have the greatest impact on happiness.

https://doi.org/10.1371/journal.pone.0144414.t005

Happiness or a sense of happiness is an advanced human psychological experience. As the ultimate goal and ideal of life, happiness has a unique meaning. All human acts are a pursuit of happiness and are influenced by people’s imagined models of happiness. Therefore, different cultural backgrounds, environments and faiths may lead to different understandings of and attitudes toward happiness. Personal outlooks on life, values, and the world guide people to consciously pursue happiness. Nurses are a special group of individuals who work under high pressure, and hospital administrators have always struggled to find ways to relieve nurses’ occupational stress and prevent job burnout. Humanized management ideas can be applied to both the recipients of services and the medical nursing staff who provide services. From the perspective of psychology, happiness research mainly refers to the happiness index. The introduction of the happiness index provides a new way of thinking about the development of nursing manpower capital [ 14 ] that can improve nurses’ well-being and help them approach their work with physical and mental pleasure. All of these results would have significant effects on the provision of high-quality nursing service. This study shows that happiness is a type of comprehensive subjective feeling, and nurses’ happiness is influenced by a variety of internal and external factors.

Nurses Responses Indicate a Moderate Level of Happiness

This study found that the general happiness index of nurses is at a moderate level. This finding indicates that local nurses’ physical and psychological conditions are not ideal, as the scores for the nine dimensions of the MHQ show large data range.

Regarding comprehensive well-being, the lowest and second-lowest scoring dimensions were negative emotions and life satisfaction, respectively. Negative emotions are a tremendous challenge for nurses because of tension between doctors and patients; daily encounters with patients afflicted by disease; patients’ mental status; nurses’ educational levels; and the many different patient needs that require responses and care from the nurses. Additionally, changes in the patients themselves and their families evoke sadness, anger and other negative emotions [ 15 ], which can actively affect nurses’ emotional well-being and have a negative impact on their work. The quality of the relationships among the factors that contribute to nurses’ general well-being is very important; in fact, these relationships have a greater impact on nurses’ happiness than their salaries do [ 16 ]. The low scores in life satisfaction indicate that the nurses are not satisfied with their overall living conditions, and their life aspirations and needs are not well met. Low scores for life satisfaction may be associated with lower income levels, limited time for rest or entertainment, reduced time with family, and other personal factors.

The highest and second-highest scoring dimensions were health concerns and friendly relationships. The high scores for health concerns indicate that nurses are concerned about their health; they want to maintain a good life, and their concern about their own health may be related to their working conditions and occupational characteristics. Every day, nurses encounter large numbers of unwell people, and their resulting awareness of the importance of health hazards and diseases may lead to this strong sense of concern about their own health. Regarding the high scores for friendly relationships, nurses have relatively harmonious interpersonal relationships, and excellent interpersonal skills can improve morale, resulting in a collective spirit of cohesion [ 17 ] and a good atmosphere among the nursing team, which would emphasize the positive effects of nursing.

Analysis of the Factors that Influence the Nurses’ Happiness Index

The WHO definition of health is as follows: “Health is a state of complete physical, mental and social adaptation and not merely the absence of disease and weakness”. Health care professionals’ physical and mental health provides the foundation for their ability to provide patient services, and nurses realize the value of a high quality of life. This approach to defining a good life has come to be called “subjective well-being” (SWB) [ 18 ]. SWB is an aspect of the comprehensive sense of happiness. SWB has some relevance because it can help people find happiness, provide optimal stimulation and positive social contact, and produce a social identity. For female nurses, as their social roles change and their range of available occupations expands, their life satisfaction and job satisfaction strengthens [ 9 , 19 ]. Job satisfaction, to some extent, affects the happiness index.

Emotional factors.

Emotional factors also affect nurses’ happiness index. SWB is a subjective experience, and objective factors do not directly affect subjective well-being; however, through positive emotions, personal growth and other subjective experiences, subjective well-being is influenced indirectly [ 12 , 20 ]. This study found that nurses are prone to burnout [ 21 ] after working several years in the field. In Lebanon, burnout is particularly common and severe among working nurses; there is a significant correlation between burnout and nurses’ mental health [ 22 ]. "The health care light" and other historical societal factors do not look at the nursing profession accurately; nurses are not receiving the proper respect in work or life, and nurses receive less social support, which they can receive from all types of family units. In the eyes of the majority of patients and their families, physicians decide who is to be master over their health or survival. Nurses in health care often work in a passive, subordinate position associated with increased respect toward doctors; under such circumstances, nurses are treated with apathy and may even be manhandled or disrespected. As a result of high risk factors coupled with the serious shortage of domestic nurses, the ratio of nurses to beds is less than 0.4:1. The nursing workload, imminent burnout, tension between doctors and patients and other adverse psychological conditions contribute to an increase in negative emotions and decreased self-identity among nurses. Karimi’s research study demonstrate the importance of emotional intelligence and presenteeism effects on nurses’ well-being; and we should require more nursing training and development to be done in relation to emotional intelligence [ 23 ].

Support factors.

Social support means a nurse is recognized by society, only if nurses are satisfied with their work and life and have harmonious interpersonal relationships, social support can really provide help on material or information to increase their happiness, satisfaction, sense of belonging and ability of handling emergency. Positive social role of support, can improve nurses’ overall emotional index. Especially strong support from work can reduce the incidence of job burnout and simultaneously inspire nurses to learn from each other than further enhance SWB experience. Life satisfaction depends on the family environment, marital relationships and other personal factors. Studies and surveys show that marriage can improve peoples’ happiness level; the subjective happiness levels of those who are married are higher than those who are unmarried, divorced, separated or widowed [ 24 ]. Lee found that when nurses leave their jobs because of burnout and interpersonal conflict, the resulting workload shifts require the remaining nurses to work excessive hours with increased psychological stress. These factors can negatively impact families, especially marital relationships, because the clinical front line is busy all day and nurses have few opportunities to communicate with their family and friends or to talk about their confusion and distress regarding their income or their satisfaction with the attention they receive from their family. There is a significant correlation between the emotional relationship between husband and wife and the happiness index [ 25 ]. Therefore, improvements in income satisfaction, improvements in marital relations, the effective and timely resolution of negative emotions, and continued good health are important for enhancing a nurse’s happiness index. Psychology research has found that good relationships and self-reported happiness are the most important determinants of the happiness index. Aristotle described people as “social animals” to emphasize the importance of human relationships; thus, a long-term intimate relationship is a main goal pursued by most people. Mutual appreciation among colleagues, mutual gratitude, mutual love and mutual support ease the psychological pressure of nurses to some extent and improve their relationships with others [ 26 ]. Harmonious interpersonal relationships among colleagues help individuals maintain a good general state of emotion and protect them under stress. Diener found that very happy people have rich and satisfying social relationships, and good social relationships are universally important to the human mood [ 27 ].

Stress factors.

Nursing workers are always busy at clinical front line, rarely with family or friends to talk to their confusion and distress, when they are confronted with plight, competition and risk, their stress response is also an important factor to influence happiness, support cannot effectively ease the pressure on the impact of the SWB. In addition, high anxiety levels about workplace violence and certain types of work were associated with experiences of violence; interventions to minimize workloads and improve nurse-patient relationships are essential to combat depressive symptoms among nurses [ 28 , 29 ]. Violence, especially in the medical workplace, can cause direct physical or psychological harm to nurses, create a violent shadow victim mentality, and lead to considerable psychological pressure [ 30 , 31 ], loss of motivation, and fewer happy experiences.

Professional titles factors.

Professional titles are closely related with and material benefits, occupational status and career achievements. Nurses with high titles are mostly the backbone of the department or hospital and have a good self-control and sound social adaptability, namely, the higher the professional titles, the higher happiness index. Unit leadership that creates empowering workplace conditions plays a key role in establishing supportive practice environments that increase work effective-ness and improves well-being [ 32 ]. But for primary nurses, they get a slow promotion for a variety of reasons, so if there are more opportunities and platforms being offered, it will certainly help to improve the nurses’ happiness.

Other factors.

Many other factors are affecting nurses’ happiness index as well, life vitality, self-value, altruistic behavior, personality growth to name only a few. Nursing job reflects their value of life, their passionate vitality of life, selfless dedication and a sound personality growth which all involves love, and that love propels nurses to experience happiness when they take care of patients.

In addition, a lot of research and studies have shown that empathy and comfort can effectively relieve fatigue and pain, improve nurses' well-being, and let nursing workers to work better [ 33 – 35 ]. For those nurses who are working on the frontline of clinical medicine, their happiness index is an important problem drawing attention from all walks of life. The asset-based paradigms of positive psychology offer new approaches for bolstering psychological resilience and promoting mental health [ 36 ]. Health administrators and policy-makers would like to enhance the work related to positive emotional experience by coping strategy and proper intervention, especially from the perspective of positive psychology.

The sample size of 206 respondents from twelve hospitals in one city may limit the study’s power. While this sample was adequate for our analysis, it is insufficient to allow for a more detailed analysis of differences in the workplace and happiness indexes across departments in different area hospitals. This cross-sectional study is limited to a small city in central China because of a lack of resources and time.

In short, subjective well-being is a positive feeling and level of psychological awareness that is related to nurses’ mental health. If nurses’ subjective well-being can be effectively improved, burnout among nurses may be alleviated. Health service managers should measure and understand nurses’ happiness, consider the factors that affect nurses’ happiness, and provide active psychological counseling and care to promote nurses’ enthusiasm and inspire their dedication. Doing so would help nurses achieve true job satisfaction and promote their loyalty to their workplace.

Supporting Information

S1 table. data of questionnaire entry..

Survey database.

https://doi.org/10.1371/journal.pone.0144414.s001

S2 Table. Ethical approval.

Ethical approval was given by the Medical Ethics Committee of Wuhan University School of Medicine, P.R. China.

https://doi.org/10.1371/journal.pone.0144414.s002

S3 Appendix. This file includes the following Tables 1 , 2 , 3 , 4 and 5 .

https://doi.org/10.1371/journal.pone.0144414.s003

Acknowledgments

Project funding: This project was funded by the National Natural Science Foundation of China (Grant No. 81273179) and the scientific research project for university student of Hubei University of Medicine (Grant No. 2011XS16), and as a summer social practice project of the Institute of Medicine and Nursing in 2011. While we were conducting this study, we received warm receptions and support from the medical institutions of Hanchuan City in the Chinese Province of Hubei. We are very grateful to the nurses who participated in this study and to all of the field investigators. We also thank Professor Yuanjiang Miao (Nanchang University) for his survey instrument. We would also like to acknowledge our friends Zhen He and Bing Yang for their valuable assistance in our study.

Author Contributions

Conceived and designed the experiments: CHY RTM. Performed the experiments: RTM YL CHY BL YH. Analyzed the data: RTM YL. Contributed reagents/materials/analysis tools: RTM YL CHY BL YH. Wrote the paper: RTM CHY. Reviewed the manuscript: YL BL.

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• 13. Miao Y. Happiness in psychology filed: Research into the theory and measurement of well-being. Unpublished Ph D thesis, Nanjing Normal University (in Chinese). 2003.

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• Published: 09 September 2022

The reliability, validity and screening effect of the happiness index scale among inpatients in a general hospital

• Yizhong Shen 1 , 2 ,
• Shuai Yuan 1 ,
• Jingwen Liu 1 ,
• Bin Sun 1 ,
• Zilin Chen 1 ,
• Lijiao Zheng 3 ,
• Lihao Chen 3 ,
• Hanwei Chen 3 ,
• Huiqiang Feng 4 &
• Hongbo He 1  

BMC Psychiatry volume  22 , Article number:  601 ( 2022 ) Cite this article

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A Correction to this article was published on 22 September 2022

This article has been updated

The Happiness Index Scale (HIS) is a newly developed scale by our group to screen for common psychological illnesses among general hospital inpatients. This study aimed to analyze the reliability, validity and screening effect of the HIS and to explore its clinical application.

From April 1, 2021, to December 31, 2021, a total of 8405 continuous inpatients were enrolled from different departments of a large tertiary general hospital with 1385 inpatient beds in Guangzhou, Guangdong Province, China. Using a cross-sectional survey design, each participant was assessed with the Patient Health Questionnaire 9(PHQ-9), Generalized Anxiety Disorder 7 items(GAD-7), Athens Insomnia Scale (AIS), Columbia Suicide Severity Rating Scale (C-SSRS) and HIS within 24 h of admission. McDonald's ω coefficient, the Guttman split-half coefficient and the test–retest reliability coefficient were used to evaluate the reliability of the HIS and the construct validity and criterion validity of the validity tests. Scores on the PHQ-9, GAD-7, AIS, and C-SSRS were used as the gold standard tools to analyze the screening effect of the HIS.

The HIS exhibited very good reliability, with a McDonald's ω coefficient of 0.825, a Guttman split-half coefficient of 0.920 and a test–retest reliability coefficient of 0.745 ( P  < 0.05). Confirmatory factor analysis showed a satisfactory model fitting index with a χ 2 /df = 2.602, a root mean squared error of approximation (RMSEA) of 0.014, a standardized root mean square residual (SRMR) of 0.010, a comparative fit index (CFI) of 0.992, and a Tucker–Lewis index (TLI) of 0.983. The correlation coefficient between the total score of each dimension of the scale and the corresponding criterion was 0.854 ~ 0.949 ( P  < 0.001). The HIS showed a very good distinguishing effect. The average HIS score of inpatients who screened positive for psychological problems was significantly higher than that of inpatients who screened negative for psychological problems ( t  = 3790.619, P  < 0.001). The effect size was very large (Cohens d  = 2.695, 95% CI  = 2.630 ~ 2.761). Approximately 90.2% of the positive and negative screening results of the HIS were matched with the gold standard tools, with a kappa value of 0.747 ( P  < 0.001). The screening effect test showed a sensitivity (true positive rate) of 92.9% and a specificity (true negative rate) of 89.5%.

The HIS exhibited satisfactory reliability and validity and a clinically meaningful screening effect with a much shorter version compared to the commonly used screening scales. Thus, it could potentially be useful as the first screening step to rule out psychological conditions for inpatients in general hospitals or to remind medical teams of further psychological concerns.

Peer Review reports

The deterioration of physical health often causes great psychological pressure, and mental health problems are common in individuals with physical illness in the general population [ 1 , 2 , 3 , 4 ]. Studies have shown that the prevalence rate of mental disorders is significantly higher in general hospital inpatients with relatively critical and complex physical illnesses (43.7%) than in physically healthy subjects (25.0%) [ 5 ]. In China, at least 17.3% of medical and surgical outpatients are reported to have at least one psychiatric disorder [ 6 ]. The results of Kroenke's study found that depression and anxiety disorders were the most prevalent psychological disorders, both in general hospital and specific patient groups, with a prevalence of at least 5 to 10% and frequent comorbidities [ 7 ]. And depression and anxiety disorders are highly correlated with insomnia [ 8 ]. Meanwhile, mental illness can exacerbate or worsen an existing physical illness, lead to slow recovery [ 9 , 10 ], and increase the risk of social dysfunction and suicide [ 11 , 12 , 13 ]. According to some statistics, 43% of the individuals who commit suicide in China suffered from serious physical diseases [ 14 ]. We screened 916 inpatients from a large tertiary general hospital for 2 days and found that 37.0% of the inpatients had at least one psychological problems, including 23.8% with depression, 15.4% with anxiety, 28.1% with insomnia and 5.4% with suicide risk [ 15 ]. As mentioned above, psychological problems such as depression, anxiety, insomnia and suicide risk are prevalent among inpatients in general hospitals.

Although mental illness is prevalent among general hospital inpatients, most general hospitals in China do not yet have psychiatric departments [ 16 ]. It has been reported that nonpsychiatrists in general hospitals usually pay more attention to physical diseases when diagnosing and treating patients, and they often ignore mental health problems, resulting in a generally low recognition rate of psychological disorders among general hospital inpatients [ 17 ]. A screening of depression and anxiety disorders in 15 general hospitals in China showed that about 16.5% of inpatients were screened for depression or anxiety disorders, of which only 8.5% were recommended for psychiatric consultation, only 6.4% received psychiatric pharmacological interventions, and most patients (80.8%) received only routine management of their own somatic diseases [ 18 ]. Screening of depressive disorders in outpatient internal medicine departments of 23 general hospitals in Shenyang showed that approximately 11.0% of patients were screened for co-morbid depressive disorders, of which only 4.0% were diagnosed by clinicians and only 3.0% were treated with antidepressant medication [ 19 ]. These surveys showed that non-psychiatrists in Chinese general hospitals had significantly lower recognition rates of mental disorders than in Western countries (32.5–64.3%) [ 20 , 21 , 22 ].

Meanwhile, the somatic symptoms of mental disorders may mask the subjective manifestations of the original mental disorder [ 23 ]. It has been reported that more than two-thirds of patients with depression and/or anxiety disorders initially visit general hospitals only for somatic symptoms such as dizziness, headache, palpitations, chest pain, fatigue, insomnia and abdominal pain [ 24 ], resulting in a high rate of misdiagnosis, as patients with mental disorders are easily misdiagnosed as having other organic diseases. A study on the misdiagnosis of psychiatric disorders in general hospitals investigated 1062 patients with psychiatric disorders who had been seen in general hospitals. The results showed that 45% of the study subjects were initially seen in other nonpsychiatric outpatient clinics, with a misdiagnosis rate of 42% in the full sample; 6.3% of the patients had been hospitalized due to misdiagnosis, with 40% of them being hospitalized more than twice [ 25 ]. Therefore, most nonpsychiatric department inpatients have mental illnesses that are left untreated or misdiagnosed, which not only wastes medical resources but also adds to the financial and mental burden of patients while causing tension between doctors and patients [ 26 ].

On the other hand, a shortage of psychiatric professionals in general hospitals in China is common, with only 43.19% of secondary and tertiary general hospitals having a psychiatric department [ 16 ]. There is a relative lack of psychiatric consultations in general hospitals, and one study reported a psychiatric consultation rate of 0.6 ~ 1.26% in general hospitals in China [ 27 , 28 , 29 ], which is lower than that reported to be approximately 2.6 ~ 3.3% in western countries [ 30 , 31 ]. For general hospital patients with comorbid psychiatric disorders, general hospital nonpsychiatrists are often their primary care physicians. However, nonpsychiatrists in general hospitals lack practical experience in the diagnosis and treatment of psychiatric disorders and are particularly unskilled at identifying patients with psychiatric disorders with significant somatic symptoms [ 32 ]. As a result, such patients are often underdiagnosed or misdiagnosed, leading to delays in treatment. Such delays can lead to a number of problems, the most serious of which is an increased risk of suicide.

The identification and treatment of mental disorders are receiving increasing attention in China. The "Health China 2030 Planning Outline" document proposes that establishing an effective screening and intervention system for known high-risk groups and improving the capacity of mental health services in medical institutions are the pressing key tasks [ 33 ]. In 2021, Guangdong Province officially piloted the "Guangdong Happy Hospital (GHH)" project, which aims to improve the early recognition rate of mental disorders among inpatients, and to improve the mental health service capacity of comprehensive medical institutions [ 34 ]. However, psychiatrists in general hospitals have limited resources for conducting a psychological assessment interview for each inpatient [ 16 ]; therefore, the initial intention of our project was to develop the HIS as an initial screening tool and to complete an initial screening of each patient prior to hospitalization by the HIS within 1 min. After the patients who screened positive entered the ward, the technically trained "Happiness nurse" conducted a detailed interview and psychological assessment and ultimately performed graded and classified specialty interventions based on the assessment results.

The psychological assessment scales are important tool for initial screening to identify a patient’s mental health status, it has the advantages of objective results, quantitative description, clear rating, and economic convenience [ 35 ]. Patients with psychological distress may perceive less evident stigma when reporting their problem to the physicians. It also could help the non-psychiatric physicians pay more attention to the psychological distress in limited time and manage it in suggested procedure according to the results [ 36 ]. However, the current problem is that most psychiatric self-assessment scales only cover one assessment dimension; to assess a patient’s mental health status in multiple dimensions, multiple scales need to be used together, which can lead to a large number of items measured by patients and take a long time, which is inappropriate for busy clinical staff and anxious patients [ 36 , 37 ]. There are relatively few reports on whether the multi-dimensional scales in foreign countries are applicable to general hospitals in China, and it may be influenced by cultural differences in the interpretation and expression of scale content that are difficult for Chinese patients to understand [ 38 ]. Based on these issues and combined with the prevalence of depression and anxiety, insomnia and suicide risk in general hospital patients, we extracted items from the different dimensional scales available that are internationally recognized and applicable to clinical screening in China. Namely, the PHQ-9, GAD-7, AIS and C-SSRS [ 39 , 40 , 41 , 42 ], and developed a concise primary screening scale, the HIS, which has multiple dimensions, fewer entries, and fewer time-consuming features [ 34 ]. The scale includes 8 items covering 4 factors: depression, anxiety, insomnia and suicidality. The development of the HIS allows for rapid initial screening to identify general hospital inpatients with possible psychological problems to provide a basis for subsequent comprehensive psycho-physiological assessment and intervention by licensed psychiatrists for inpatients who screen positive for psychological problems and improving the scarcity of psychiatric resources.

In a previous study, we validated the HIS with good reliability and validity by analyzing psychometric data from 458 nonpsychiatric inpatients in Guangzhou, China [ 34 ]. For subsequent application at a larger scale, in this study, we expanded the sample size to include all inpatient units in a large tertiary general hospital in Guangzhou, China, to assess the reliability and validity of the HIS using psychometric data from 8405 nonpsychiatric inpatients to provide a more robust basis for the use of the HIS for the clinical screening of inpatients in general hospitals and to verify its feasibility in different clinical departments.

Materials and methods

Study sites and participants.

The study was conducted in Guangzhou Panyu Central Hospital, a 1385-bed tertiary general hospital in Guangzhou, Guangdong Province, which is the 3 rd largest city in China, with a population of approximately 127 million. The inclusion criteria were: patients with a clear consciousness and understanding of the contents of the questionnaire; patients who provided informed consent for voluntary participation in this survey. The exclusion criteria were: patients who suffered from serious physical diseases and were unable to complete the questionnaire; patients who were undergoing surgery. A total of 9038 inpatients were enrolled, including 8405 valid questionnaires. This study was approved by the Ethics Committee of the Affiliated Brain Hospital of Guangzhou Medical University and Guangzhou Panyu Central Hospital. Each patient was provided with an electronic informed consent after the researcher was informed in person of the content and purpose of the study.

Data collection

Twenty-eight nurses from 28 inpatient wards participated in this study, and each of the nurses was responsible for data collection from their ward in Guangzhou Panyu Central Hospital. All nurses received 12 h of face-to-face course training, including training on project introductions, ethical issues of the survey, psychological assessments, common psychological symptoms and common psychiatric diseases, common psychological intervention methods, etc. After patient admission, the participating nurse informed the patient of the content and purpose of the research project. Under instruction, the patient scanned the project bar code with their own cell phone to enter the “questionnaire star” application. If the patient agreed to participate in the survey, they clicked the informed consent option and proceeded to the formal answer interface; if the patient did not agree to participate, they clicked the reject answer option and exited the application. The survey was conducted in a relatively quiet independent ward. All patients answered the questionnaires within 24 h of admission, and the evaluation took 10 ~ 15 min. In addition, 87 randomly selected participants were evaluated with the same scale again after a 2–3 week inpatient stay for the test–retest reliability test.

Assessment tools

The sociodemographic data including gender, age, marital status, education level, monthly income and inpatient department of all the participants were collected.

The HIS scale [ 34 ]: The HIS is obtained by extracting 8 items from 31 items in PHQ-9, GAD-7, AIS and C-SSRS by GHH project team through exploratory factor analysis [ 34 ]. The psychometric properties of the scale have been initially validated in a sample of 458 cases [ 34 ]. The scale covers 4 factors, each containing 2 entries. The eigenvalues of the four factors ranged from 0.79 to 3.30 and the four factors explained 84.2% of the total variance in the results. The factor loading of each entry is greater than 0.8. Factor 1 includes 2 items from the PHQ-9 related to the core items of depression(HIS-1: Little interest or pleasure in doing things; HIS-2: Feels down, depressed, or hopeless). Factor 2 includes 2 items from the GAD-7 related to anxiety(HIS-3: Worries too much about different things; HIS-4: Becomes easily annoyed or irritable). Factor 3 includes 2 items from the AIS related to sleep quality(HIS-5: Total sleep time; HIS-6: Total sleep quality (no matter how long you sleep). Factor 4 includes 2 items from the C-SSRS related to suicidal thoughts(HIS-7: Do you actually have some thoughts about suicide?; HIS-8: Have you been thinking about how to kill yourself?). Among the 8 items of the scale, Items 1 ~ 6 are scored with 0 ~ 3 points (0 = "never", 1 = "a few days", 2 = "more than half the days", 3 = "almost every day"), and Items 7 ~ 8 are answered with "yes" or "no". Each score is multiplied by the corresponding weight and summed to obtain the actual score of the scale. See additional file 1 for details on how weights are calculated. Plotting ROC curves to determine cutoff values of scale scores [ 34 ]. The total score of the final scale ranges from 0 ~ 6.247. A total score of 0 ~ 0.364 indicates no psychological problems, 0.365 ~ 0.954 indicates mild psychological problems, 0.955 ~ 1.469 indicates moderate psychological problems, and 1.470 ~ 6.247 indicates severe psychological problems. See Table 1 for details.

The entries of the HIS were extracted from a total of 31 entries of the PHQ-9, GAD-7, AIS, and C-SSRS. The PHQ-9, GAD-7, AIS and C-SSRS were used as the criteria to analyze the criterion validity of the HIS. The specificity and sensitivity of the HIS were calculated using the above four classical self-assessment scales as the "gold standard".

The evaluation result of the "gold standard" was defined as follows: If at least one of the abovementioned four classical self-assessment scales had mild results or higher, it represented positive results for psychological problems in the "gold standard" evaluation. The HIS rated psychological problems as mild or higher as indicative of positive results for psychological problems.

Statistical analysis

Based on the factor structure of the exploratory factor analysis in the previous development of the HIS, this study used a larger sample size to test the structural validity of the scale through confirmatory factor analysis. Pearson correlation analysis was used to test the criterion validity of the scale. The intergroup difference in the HIS average score of the inpatients who screened positive or negative for psychological problems by the "gold standard" was tested by the Welch's robust T-Test. Descriptive parameters using Cohen’s d for indicating effect size were used. McDonald's Omega and the Guttman split-half coefficient were used to test the internal consistency reliability of the scale, and Pearson correlation analysis was used to test the test–retest reliability of the scale. The sensitivity and specificity of the HIS were calculated by screening effect analysis. P  < 0.05 indicated that the difference was statistically significant. Descriptive statistics were computed utilizing the statistical software IBM SPSS Statistics 22.0 while confirmatory factor analysis was conducted with the R package Lavaan.

General demographic data

The age of inpatients was mostly concentrated 18–59 years old (5348 patients; 63.6%), and there were more female inpatients than male inpatients (4420 patients; 52.6%). The education level of the inpatients was low, and 61.6% of the subjects had been educated for 9 years or less. Married inpatients significantly outnumbered unmarried inpatients. Slightly more inpatients earn less than 5,000 RMB per month. All subjects were from nonpsychiatric departments, of which there were more inpatients in the Surgery department (42.5%; see Table 2 at the end of the article for details).

Welch's T-test showed that inpatients with female (0.400 ± 0.662), more than 9 years of education (0.395 ± 0.631) and unmarried (0.455 ± 0.717) had relatively higher HIS scores. Multiple comparisons showed that the HIS scores of inpatients in internal medicine (0.387 ± 0.660) and obstetrics/gynecology (0.438 ± 0.613) were higher than those in surgery (0.283 ± 0.579), and those in obstetrics/gynecology were higher than those in ophthalmology/otolaryngology (0.328 ± 0.625). HIS scores of inpatients aged 18–59 years (0.367 ± 0.626) were higher than those aged 60 years or older (0.313 ± 0.606).

Validity evaluation results of the HIS

Construct validity.

Factor structure based on exploratory factor analysis from the results of the development of the HIS [ 34 ]. This study conducted confirmatory factor analysis to evaluate the structural validity based on 8405 study participants. With eight entries as observed variables and four factors as latent variables, the loadings for each entry and the correlations between the factors are shown in Fig.  1 , with the factors significantly uncorrelated ( r  = 0.01–0.14, P  < 0.001). The fitting index results of the various models showed a χ 2 /df of 2.602, an RMSEA of 0.014, a CFI of 0.992, a TLI of 0.983, and a SRMR of 0.010. The results showed that the fit index of the model was satisfactory. It can be concluded that the scale has high structural validity (see Fig.  1 for the structural equation model).

4-factor structural equation model. Note. Parameter estimation methods for confirmatory factor analysis using weighted least squares means and variances (WLSMV). The loadings in the figure are normalized. All loadings in figure are significative. * p  < .001

Criterion validity

As described in the Methods section, the HIS included 4 factors (depression, anxiety, insomnia and suicide), with 2 items for each factor. The PHQ-9, GAD-7, AIS and C-SSRS were used as the criteria to predict the criterion validity of the HIS. Pearson correlation analysis showed that the total score of each factor of the HIS was highly positively correlated with the total scores of the corresponding classical scales. The correlation coefficients were as follows: depression: r  = 0.854; anxiety: r  = 0.935; insomnia: r  = 0.921; and suicide: r  = 0.949 ( P  < 0.001). The details are shown in Table 3 .

Distinguishing effect

The "gold standard" screened 1896 inpatients with psychological problems and a mean HIS score of ( 1.207 ± 0.776); 6509 inpatients without psychological problems and a mean HIS score of ( 0.098 ± 0.208). The Welch's robust T-Test showed that inpatients with psychological problems had significantly higher HIS scores than those without psychological problems ( t  = 3790.619, P  < 0.001). The effect size was very large (Cohens d  = 2.695, 95% CI  = 2.630 ~ 2.761). The results showed that the HIS could clearly distinguish between inpatients with and without psychological problems, indicating a high distinction effect of the HIS. Descriptive statistics were done for each item, which includes mean, standard deviation, measures of skewness and kurtosis as shown in Table 4 .

Reliability evaluation results of the HIS

The McDonald's omega of the HIS were 0.825, and the McDonald's omega for each factor ranged from 0.798 ~ 0.891 ( P  < 0.05), indicating that the items of the scale had high internal consistency.

The divided half reliability coefficient model was used to analyze the scale, and the items were randomly divided into two groups. One group comprised the HIS-1, HIS-3, HIS-5 and HIS-7. The other group comprised the HIS-2, HIS-4, HIS-6, and HIS-8. The results showed that the Guttman split-half coefficient of the HIS was 0.920, and that of each factor ranged from 0.720 ~ 0.891 ( P  < 0.05), indicating that the Guttman split-half coefficient of the scale was high.

Eighty-seven randomly chosen patients were evaluated with the HIS twice (with an interval of 2–3 week) to evaluate the test–retest reliability. The results showed that the correlation coefficient of the total scores of the two groups was 0.745, and the correlation coefficient of the scores of the two groups for each factor ranged from 0.640 ~ 0.863 ( P  < 0.05). The test–retest correlation coefficient of each test score of the HIS showed that the consistency between the retest score and the initial test score was high, and the stability of the HIS was high (see Table 5 for details).

Analysis of the HIS screening effect

Screening with the HIS detected 5959 (70.9%) patients without psychological problems and 2446 (29.1%) patients with psychological problems. Compared with "gold standard" screening, "gold standard" detected 6509 (77.4%) patients without psychological problems and 1896 (22.6%) patients with psychological problems. A screening effect analysis was conducted, and indicators of the accuracy of screening with the HIS were compiled. The screening consistency rate between the HIS and "gold standard" was 90.2% [(5824 + 1761)/8405], and the kappa value was 0.747 ( P  < 0.01). The HIS assessment was in high agreement with the "gold standard" (see Table 6 ). The sensitivity (true positive rate) of the HIS was 92.9%, and the specificity (true negative rate) was 89.5%. The missed diagnosis rate (false negative rate) was 7.1%, and the misdiagnosis rate (false-positive rate) was 10.5%. The positive predictive value (PPV) was 72.0%, and the negative predictive value (NPV) was 97.7%.

This study used data from 8405 non-psychiatric inpatients in Guangzhou, China to evaluate the reliability, validity and screening effect of HIS. Extensive evaluation showed that this scale has satisfactory reliability and validity as well as screening effects. Meanwhile, depression and anxiety disorders are prevalent and highly comorbid in general hospitals, with a greater chance of insomnia and suicide risk in severe cases [ 43 , 44 ]. Therefore, we developed the HIS to obtain a single score about general distress that summarizes the core features of depression and anxiety and includes a high generalization for insomnia (sleep duration and total sleep quality) and two entries regarding suicidal thought. The HIS thus reasonable based on its content. It is also an improvement on all of the above issues, as reflected by the inclusion of multiple dimensions, a smaller number of entries, and simplicity of use.

Our group conducted an exploratory factor analysis on data from 458 previous patients [ 34 ], and considering that the scale needs to contain 4 dimensions common in general hospitals (depression, anxiety, insomnia and suicide risk). Therefore, the number of factors was set to four before exploratory factor analysis, and four factors with eigenvalues in the range of 0.79–3.30 were obtained. Usually the factor eigenvalues need to be greater than 1, but the cumulative variance contribution of the four factors was 84.2%, and the factor loadings of all eight entries were above 0.8, which generally met the statistical criteria. In this study, based on the structure of the four factors obtained from the exploratory factor analysis in the above development results [ 34 ], a validation factor analysis was conducted, and the structural equation model showed that the four factors were independent of each other ( r  = 0.01–0.14, P  < 0.001) and the structural model equation fitted well, indicating satisfactory structural validity of the scale. Figure  1 shows that the depression factor was not significantly correlated with the suicide factor, which may be due to the fact that general hospital inpatients are non-psychiatric inpatients and the risk of suicide may be related to a variety of factors such as their own serious physical illness, the social environment in which they live, or other psychiatric symptoms [ 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ]. However, the original purpose of the HIS was rapid primary screening, and it was not possible to include various risk factors in the scale, so this scale includes suicide as a separate factor to screen patients for psychological problems in order to provide a basis for a comprehensive assessment by subsequent professionals. The correlation between HIS and PHQ-9, GAD-7, AIS and C-SSRS further demonstrated the satisfactory criterion validity of the HIS ( r  = 0.854–0.949, P  < 0.001). The average HIS score of inpatients with psychological problems was significantly higher than that of inpatients without psychological problems ( t  = 3790.619, P  < 0.001), and the effect size was very large (Cohens d  = 2.695, 95% CI  = 2.630 ~ 2.761); that is, the distinction effect of the HIS was very good, and it can effectively distinguish inpatients with psychological problems from inpatients without psychological problems. In summary, inpatients with psychological problems can be more clearly and accurately identified by psychiatrists through HIS, which is convenient for subsequent timely psychological evaluations and graded interventions and avoids the waste of medical resources.

To support construct validity, an analysis of variance was applied to test the interrelations among the psychological health level and socio-demographic variables. The results showed significant differences in mean HIS scores between the gender, age, years of education, marital status, and disease type (inpatient department) groups. One possible explanation for this difference is that psychological health baseline and psychological resilience levels differ across demographic characteristics. Several surveys in China have found that women, unmarried and middle-aged people have relatively low levels of mental health [ 53 , 54 , 55 , 56 ]. Meanwhile, the proportion of adult patients in this study sample was large, and there were only 149 (1.8%) patients under 18 years old, so whether HIS is applicable to children and adolescents needs further validation in the future.

In this study, the internal consistency of all 8 items was 0.825, and the internal consistency of the factors was generally higher than the accepted value of 0.70. The Guttman split-half coefficient was 0.920, which also proved that the scale has high reliability. There were 87 participants who were administered the HIS twice (2–3 weeks apart) to evaluate the test–retest reliability. The correlation coefficient of the two scores was 0.745 ( P  < 0.001), the consistency and stability of the scale before and after measurement are high. The retest reliability of the suicide dimension was slightly lower, but still acceptable, and the reason considered may be that the risk of suicide in general hospital inpatients changes as their somatic illness improves or worsens [ 12 , 13 , 57 ].

This study analyzed the screening effect of the HIS on inpatients' psychological problems by comparing the evaluation results to the "gold standard". The overall consistency rate between the calculated HIS score and the "gold standard" was 90.2% (Kappa = 0.747, P  < 0.01)). This shows that the evaluation effect of the HIS is highly consistent with the "gold standard", indicating that the HIS can effectively predict the psychological problems of nonpsychiatric inpatients and that the feasibility of its clinical operation is high. The high true-positive rate and low false-negative rate indicate that the HIS has a high accuracy in screening patients with positive results, which can reduce missed diagnoses in initial screenings and provide timely and effective psychological interventions to high-risk patients accordingly. A higher true-negative rate and a lower false-positive rate reflect the high accuracy of the HIS in narrowing the range of patients with positive results. If the false-positive rate is high, more medical costs and resources will be consumed, and an assessment tool with a high specificity and low misdiagnosis rate is more desirable in current society where mental disorders are highly stigmatized. In conclusion, the evaluation standard of the HIS has high sensitivity and specificity, the corresponding missed diagnosis rate and misdiagnosis rate are low, the accuracy, positive predictive value and negative predictive value results are ideal, and the scale has high diagnostic efficiency.

It is also important to note that because the psychiatric self-assessment scale is hardly the "gold standard" for diagnosing mental disorders, most clinical practices require psychiatrists to conduct the MINI interview with patients as the "gold standard" for diagnosis. However, the limited resources of psychiatrists in general hospitals do not allow them to conduct psychiatric interviews with all inpatients one by one [ 16 ], so the HIS scale, as a preliminary screening tool, can easily and efficiently initially screen out inpatients who are more likely to have psychological problems, thus providing a convenient way for subsequent psychiatrists to target patients who screen positive for definitive diagnosis and develop corresponding interventions; this would ultimately improve the identification and treatment rates of psychiatric disorders in general hospitals while avoiding the waste of medical resources and improving the current situation of lack of psychiatrists' resources in general hospitals. Our findings suggest that the HIS is a reliable and valid tool for the initial screening of nonpsychiatric inpatients in general hospitals for psychological problems for clinical and research use.

Limitations and future direction

This study is subject to several limitations. First, in this study, PHQ-9, GAD-7, AIS and C-SSRS were set as the "gold standard" to analyze the reliability, validity and screening effect of HIS; because the four abovementioned classical self-assessment scales are not 100% accurate and are not the ideal "gold standard", they are slightly less accurate as the "gold standard". Considering the large sample size of the study, the use of clinical diagnostic tools for comprehensive diagnostic assessments was inconvenient and sufficient manpower to conduct a diagnostic "gold standard" for each patient, such as the SCID, the MINI, and face-to-face visits, was lacking. At the same time, the scale was developed with the aim of conducting preliminary psychological problem screening for nonpsychiatric inpatients. Patients screened initially with positive results should be comprehensively evaluated by a psychiatrist with clinical diagnostic tools to achieve the purpose of qualitatively understanding the patients' psychological problems and graded psychological or pharmacological interventions. Second, some of the literature (e.g., Kline 2016 [ 58 ] ) suggests that each factor of the scale contains at least three entries. However, for the four factors of the HIS, each factor contains two entries, but in the results of this study, each factor (two entries) score was significantly and highly correlated with the total score of the corresponding criteria ( r  = 0.854–0.949), and the exploratory factor analysis showed that the cumulative variance contribution of the four factors (eight entries) was 84.2%, and the factor loadings of the exploratory factor analysis of all eight entries were above 0.8. Additionally, the HIS is designed to provide an easy and rapid initial screening of psychological problems in general hospital inpatients. Therefore, the impact of each factor (2 entries) on potential problems in this study was relatively small. Finally, because all participants were recruited from a general hospital in Guangzhou, China, caution should be exercised in generalizing the study results to other clinical settings or to the country as a whole. In conclusion, the HIS has satisfactory reliability, validity and screening effect when used for nonpsychiatric inpatients in general hospitals and has high feasibility for psychological illness screening of nonpsychiatric inpatients in general hospitals.

The HIS exhibited satisfactory reliability and validity and a clinically meaningful screening effect with a much shorter version compared to the commonly used screening scales, and can effectively detect patients requiring further intervention. Thus, it could potentially be useful as the first screening step to rule out psychological conditions for inpatients in general hospitals or to remind medical teams of further psychological concerns. HIS is undoubtedly of great help to the detection rate of psychological problems in general hospitals.

Availability of data and materials

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Change history

22 september 2022.

A Correction to this paper has been published: https://doi.org/10.1186/s12888-022-04264-9

Abbreviations

• Happiness Index Scale

Patient Health Questionnaire 9

Generalized Anxiety Disorder 7 items

Athens Insomnia Scale

Columbia Suicide Severity Rating Scale

Root Mean Squared Error of Approximation

Standardized Root Mean Square Residual

Comparative Fit Index

Tucker-Lewis Index

Guangdong Happy Hospital

Weighted Least Squares Means and Variances

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The authors thank all participating hospitals and clinicians for their contribution to the study.

This study was supported by a grant from the Health Science and Technology Project of Guangzhou, China (No. 2022A031003). The funding body played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

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Study Design: HHB. Data collection: SYZ, YS, LJW, CZL, ZJL, CLH, CHW, FHQ. Analysis and interpretation of data: SYZ, SB. Drafting of the manuscript: SYZ. Critical revision of the manuscript: HHB. Approval of the final version for publication: All the authors.

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Parameter estimation of linear regression model.

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Demographic distribution of the retest sample versus the total sample.

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Shen, Y., Yuan, S., Liu, J. et al. The reliability, validity and screening effect of the happiness index scale among inpatients in a general hospital. BMC Psychiatry 22 , 601 (2022). https://doi.org/10.1186/s12888-022-04219-0

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DOI : https://doi.org/10.1186/s12888-022-04219-0

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Steering Committee Co-Directors

Ray Perrault

Steering committee members.

Erik Brynjolfsson

John Etchemendy

Katrina Ligett

Terah Lyons

James Manyika

Juan Carlos Niebles

Vanessa Parli

Yoav Shoham

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Staff members.

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AI has moved into its era of deployment; throughout 2022 and the beginning of 2023, new large-scale AI models have been released every month. These models, such as ChatGPT, Stable Diffusion, Whisper, and DALL-E 2, are capable of an increasingly broad range of tasks, from text manipulation and analysis, to image generation, to unprecedentedly good speech recognition. These systems demonstrate capabilities in question answering, and the generation of text, image, and code unimagined a decade ago, and they outperform the state of the art on many benchmarks, old and new. However, they are prone to hallucination, routinely biased, and can be tricked into serving nefarious aims, highlighting the complicated ethical challenges associated with their deployment.

Although 2022 was the first year in a decade where private AI investment decreased, AI is still a topic of great interest to policymakers, industry leaders, researchers, and the public. Policymakers are talking about AI more than ever before. Industry leaders that have integrated AI into their businesses are seeing tangible cost and revenue benefits. The number of AI publications and collaborations continues to increase. And the public is forming sharper opinions about AI and which elements they like or dislike.

AI will continue to improve and, as such, become a greater part of all our lives. Given the increased presence of this technology and its potential for massive disruption, we should all begin thinking more critically about how exactly we want AI to be developed and deployed. We should also ask questions about who is deploying it—as our analysis shows, AI is increasingly defined by the actions of a small set of private sector actors, rather than a broader range of societal actors. This year’s AI Index paints a picture of where we are so far with AI, in order to highlight what might await us in the future.

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What Researchers Discovered When They Sent 80,000 Fake Résumés to U.S. Jobs

Some companies discriminated against Black applicants much more than others, and H.R. practices made a big difference.

By Claire Cain Miller and Josh Katz

A group of economists recently performed an experiment on around 100 of the largest companies in the country, applying for jobs using made-up résumés with equivalent qualifications but different personal characteristics. They changed applicants’ names to suggest that they were white or Black, and male or female — Latisha or Amy, Lamar or Adam.

On Monday, they released the names of the companies . On average, they found, employers contacted the presumed white applicants 9.5 percent more often than the presumed Black applicants.

Yet this practice varied significantly by firm and industry. One-fifth of the companies — many of them retailers or car dealers — were responsible for nearly half of the gap in callbacks to white and Black applicants.

Two companies favored white applicants over Black applicants significantly more than others. They were AutoNation, a used car retailer, which contacted presumed white applicants 43 percent more often, and Genuine Parts Company, which sells auto parts including under the NAPA brand, and called presumed white candidates 33 percent more often.

In a statement, Heather Ross, a spokeswoman for Genuine Parts, said, “We are always evaluating our practices to ensure inclusivity and break down barriers, and we will continue to do so.” AutoNation did not respond to a request for comment.

Companies With the Largest and Smallest Racial Contact Gaps

Of the 97 companies in the experiment, two stood out as contacting presumed white job applicants significantly more often than presumed Black ones. At 14 companies, there was little or no difference in how often they called back the presumed white or Black applicants.

Source: Patrick Kline, Evan K. Rose and Christopher R. Walters

Known as an audit study , the experiment was the largest of its kind in the United States: The researchers sent 80,000 résumés to 10,000 jobs from 2019 to 2021. The results demonstrate how entrenched employment discrimination is in parts of the U.S. labor market — and the extent to which Black workers start behind in certain industries.

“I am not in the least bit surprised,” said Daiquiri Steele, an assistant professor at the University of Alabama School of Law who previously worked for the Department of Labor on employment discrimination. “If you’re having trouble breaking in, the biggest issue is the ripple effect it has. It affects your wages and the economy of your community going forward.”

Some companies showed no difference in how they treated applications from people assumed to be white or Black. Their human resources practices — and one policy in particular (more on that later) — offer guidance for how companies can avoid biased decisions in the hiring process.

A lack of racial bias was more common in certain industries: food stores, including Kroger; food products, including Mondelez; freight and transport, including FedEx and Ryder; and wholesale, including Sysco and McLane Company.

“We want to bring people’s attention not only to the fact that racism is real, sexism is real, some are discriminating, but also that it’s possible to do better, and there’s something to be learned from those that have been doing a good job,” said Patrick Kline, an economist at the University of California, Berkeley, who conducted the study with Evan K. Rose at the University of Chicago and Christopher R. Walters at Berkeley.

The researchers first published details of their experiment in 2021, but without naming the companies. The new paper, which is set to run in the American Economic Review, names the companies and explains the methodology developed to group them by their performance, while accounting for statistical noise.

Sample Résumés From the Experiment

Fictitious résumés sent to large U.S. companies revealed a preference, on average, for candidates whose names suggested that they were white.

To assign names, the researchers started with a prior list that had been assembled using Massachusetts birth certificates from 1974 to 1979. They then supplemented this list with names found in a database of speeding tickets issued in North Carolina between 2006 and 2018, classifying a name as “distinctive” if more than 90 percent of people with that name were of a particular race.

The study includes 97 firms. The jobs the researchers applied to were entry level, not requiring a college degree or substantial work experience. In addition to race and gender, the researchers tested other characteristics protected by law , like age and sexual orientation.

They sent up to 1,000 applications to each company, applying for as many as 125 jobs per company in locations nationwide, to try to uncover patterns in companies’ operations versus isolated instances. Then they tracked whether the employer contacted the applicant within 30 days.

A bias against Black names

Companies requiring lots of interaction with customers, like sales and retail, particularly in the auto sector, were most likely to show a preference for applicants presumed to be white. This was true even when applying for positions at those firms that didn’t involve customer interaction, suggesting that discriminatory practices were baked in to corporate culture or H.R. practices, the researchers said.

Still, there were exceptions — some of the companies exhibiting the least bias were retailers, like Lowe’s and Target.

The study may underestimate the rate of discrimination against Black applicants in the labor market as a whole because it tested large companies, which tend to discriminate less, said Lincoln Quillian, a sociologist at Northwestern who analyzes audit studies. It did not include names intended to represent Latino or Asian American applicants, but other research suggests that they are also contacted less than white applicants, though they face less discrimination than Black applicants.

The experiment ended in 2021, and some of the companies involved might have changed their practices since. Still, a review of all available audit studies found that discrimination against Black applicants had not changed in three decades. After the Black Lives Matter protests in 2020, such discrimination was found to have disappeared among certain employers, but the researchers behind that study said the effect was most likely short-lived.

Gender, age and L.G.B.T.Q. status

On average, companies did not treat male and female applicants differently. This aligns with other research showing that gender discrimination against women is rare in entry-level jobs, and starts later in careers.

However, when companies did favor men (especially in manufacturing) or women (mostly at apparel stores), the biases were much larger than for race. Builders FirstSource contacted presumed male applicants more than twice as often as female ones. Ascena, which owns brands like Ann Taylor, contacted women 66 percent more than men.

Neither company responded to requests for comment.

The consequences of being female differed by race. The differences were small, but being female was a slight benefit for white applicants, and a slight penalty for Black applicants.

The researchers also tested several other characteristics protected by law, with a smaller number of résumés. They found there was a small penalty for being over 40.

Overall, they found no penalty for using nonbinary pronouns. Being gay, as indicated by including membership in an L.G.B.T.Q. club on the résumé, resulted in a slight penalty for white applicants, but benefited Black applicants — although the effect was small, when this was on their résumés, the racial penalty disappeared.

Under the Civil Rights Act of 1964, discrimination is illegal even if it’s unintentional . Yet in the real world, it is difficult for job applicants to know why they did not hear back from a company.

“These practices are particularly challenging to address because applicants often do not know whether they are being discriminated against in the hiring process,” Brandalyn Bickner, a spokeswoman for the Equal Employment Opportunity Commission, said in a statement. (It has seen the data and spoken with the researchers, though it could not use an academic study as the basis for an investigation, she said.)

What companies can do to reduce discrimination

Several common measures — like employing a chief diversity officer, offering diversity training or having a diverse board — were not correlated with decreased discrimination in entry-level hiring, the researchers found.

But one thing strongly predicted less discrimination: a centralized H.R. operation.

The researchers recorded the voice mail messages that the fake applicants received. When a company’s calls came from fewer individual phone numbers, suggesting that they were originating from a central office, there tended to be less bias . When they came from individual hiring managers at local stores or warehouses, there was more. These messages often sounded frantic and informal, asking if an applicant could start the next day, for example.

“That’s when implicit biases kick in,” Professor Kline said. A more formalized hiring process helps overcome this, he said: “Just thinking about things, which steps to take, having to run something by someone for approval, can be quite important in mitigating bias.”

At Sysco, a wholesale restaurant food distributor, which showed no racial bias in the study, a centralized recruitment team reviews résumés and decides whom to call. “Consistency in how we review candidates, with a focus on the requirements of the position, is key,” said Ron Phillips, Sysco’s chief human resources officer. “It lessens the opportunity for personal viewpoints to rise in the process.”

Another important factor is diversity among the people hiring, said Paula Hubbard, the chief human resources officer at McLane Company. It procures, stores and delivers products for large chains like Walmart, and showed no racial bias in the study. Around 40 percent of the company’s recruiters are people of color, and 60 percent are women.

Diversifying the pool of people who apply also helps, H.R. officials said. McLane goes to events for women in trucking and puts up billboards in Spanish.

So does hiring based on skills, versus degrees . While McLane used to require a college degree for many roles, it changed that practice after determining that specific skills mattered more for warehousing or driving jobs. “We now do that for all our jobs: Is there truly a degree required?” Ms. Hubbard said. “Why? Does it make sense? Is experience enough?”

Hilton, another company that showed no racial bias in the study, also stopped requiring degrees for many jobs, in 2018.

Another factor associated with less bias in hiring, the new study found, was more regulatory scrutiny — like at federal contractors, or companies with more Labor Department citations.

Finally, more profitable companies were less biased, in line with a long-held economics theory by the Nobel Prize winner Gary Becker that discrimination is bad for business. Economists said that could be because the more profitable companies benefit from a more diverse set of employees. Or it could be an indication that they had more efficient business processes, in H.R. and elsewhere.

Claire Cain Miller writes about gender, families and the future of work for The Upshot. She joined The Times in 2008 and was part of a team that won a Pulitzer Prize in 2018 for public service for reporting on workplace sexual harassment issues. More about Claire Cain Miller

Josh Katz is a graphics editor for The Upshot, where he covers a range of topics involving politics, policy and culture. He is the author of “Speaking American: How Y’all, Youse, and You Guys Talk,” a visual exploration of American regional dialects. More about Josh Katz

From The Upshot: What the Data Says

Analysis that explains politics, policy and everyday life..

Employment Discrimination: Researchers sent 80,000 fake résumés to some of the largest companies in the United States. They found that some discriminated against Black applicants much more than others .

Pandemic School Closures: ​A variety of data about children’s academic outcomes and about the spread of Covid-19 has accumulated since the start of the pandemic. Here is what we learned from it .

Affirmative Action: The Supreme Court effectively ended race-based preferences in admissions. But will selective schools still be able to achieve diverse student bodies? Here is how they might try .

N.Y.C. Neighborhoods: We asked New Yorkers to map their neighborhoods and to tell us what they call them . The result, while imperfect, is an extremely detailed map of the city .

Dialect Quiz:  What does the way you speak say about where you’re from? Answer these questions to find out .

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Relationships between Mindfulness, Purpose in Life, Happiness, Anxiety, and Depression: Testing a Mediation Model in a Sample of Women

Antonio crego.

1 Department of Psychology, Pontifical University of Salamanca, Calle de la Compañía 5, 37002 Salamanca, Spain; se.aspu@ebaleyrj (J.R.Y.); se.aspu@amzemogam (M.Á.G.-M.); se.aspu@amocseirp (P.R.-M.)

José Ramón Yela

María Ángeles gómez-martínez, pablo riesco-matías, cristina petisco-rodríguez.

2 Faculty of Education, Pontifical University of Salamanca, Calle Henry Collet 52-70, 37007 Salamanca, Spain; se.aspu@orocsitepc

Mindfulness is connected to positive outcomes related to mental health and well-being. However, the psychological mechanisms that account for these relationships are largely unknown. A multiple-step multiple mediator structural equation modeling (SEM) model was tested with mindfulness as the independent variable; purpose in life and behavioral activation as serial mediators; and happiness, anxiety, and depression as outcome measures. Data were obtained from 1267 women. Higher mindfulness was associated with higher levels of happiness and lower anxiety and depression symptoms. The association of mindfulness with the outcome variables could be partially accounted for by purpose in life and behavioral activation. The SEM model explained large proportions of variance in happiness (50%), anxiety (34%), and depression (44%) symptoms. Mindfulness is associated with both a sense of purpose in life and engagement in activities, which are also connected with positive outcomes. Moreover, having purposes in life is linked to higher levels of behavioral activation.

1. Introduction

The positive effects of mindfulness have been highlighted by a growing number of publications. Mindfulness-based interventions have demonstrated efficacy in the treatment of depression, anxiety, and stress-related disorders, among many other psychological problems [ 1 , 2 , 3 ]. In addition, research has found that those people reporting higher levels of dispositional mindfulness also tend to experience positive states of mind and lower levels of depression and anxiety [ 4 , 5 ].

Essentially, mindfulness involves the self-regulation of attention so that it remains focused on the present moment. Along with this self-regulation, mindfulness also requires a focus on the immediate experience with an attitude of curiosity, openness, and acceptance [ 6 ]. How can this attention-related capacity lead to beneficial consequences in terms of mental health and well-being? Several mechanisms have been proposed. For example, it has been suggested that mindfulness practices could elicit processes of decentering, value clarification, exposure, cognitive/behavioral flexibility, and self-management [ 7 , 8 , 9 , 10 ]. Others propose attention monitoring and acceptance of inner experiences [ 11 ]; emotional intelligence [ 5 ]; self-compassion [ 7 , 12 ]; and self-regulation of processes such as attention, increased body awareness, emotional regulation through reappraisal, exposure, extinction and reconsolidation, and change in one’s perspective of the self [ 13 , 14 ]. However, empirical testing of such explanatory mechanisms is still scarce, and results are often not conclusive. In this research, we argue that two variables, purpose in life and behavioral activation, may be involved in the association between mindfulness and salutary outcomes. Thus, we aimed at testing an empirically supported model intended to shed light on how mindfulness is connected with beneficial effects on mental health and happiness. In particular, we propose a link between mindfulness and having purposes in life, a variable expected to be associated with greater behavioral activation.

1.1. Purpose in Life and Behavioral Activation as Mediators between Mindfulness and Outcomes

Purpose in life may be defined as an elementary aim that serves to self-organize and inspire concrete goals, trigger behaviors, and provide meaningfulness assessments [ 15 ]. Having life purposes and valuable aims that orient one’s behavior and provide a sense of direction is mentioned as a key ingredient of a meaningful life [ 16 ]. Like mindfulness, the construct of meaning in life comprises several components. The presence of meaning in life entails being able to identify abstract, relevant, and long-term goals that guide more specific objectives and behaviors. Furthermore, a meaningful life also requires having a sense of understanding or comprehension of life, being committed to one’s personal values and that which is personally important, and a belief that life matters and is worth it, despite unavoidable suffering and pain [ 17 , 18 ].

Interestingly, the presence of meaning and purpose in life has been found to be positively associated with mindfulness. For instance, increased mindfulness has been found to be positively associated with greater presence of meaning in life, a relationship that could be explained by increased self-awareness [ 19 ], and greater knowledge of and trust in oneself and awareness of one’s strengths and weaknesses [ 20 ]. Similarly, the processes of cognitive reappraisal, wise evaluation, and discernment, which are entailed in mindfulness, have been suggested to explain assessments of life as meaningful and purposeful, the appreciation of important things, and posttraumatic growth in the face of adverse events of life [ 21 , 22 ].

Support for the connections between mindfulness and increased purpose in life comes not only from correlational research but also from experimental studies. For instance, Jacobs et al. [ 23 ] found that individuals who attended a meditation retreat on training attention skills and benevolent mental states (i.e., loving-kindness, compassion, and equanimity) showed significantly greater increases in purpose in life than controls. Similarly, Bloch et al. [ 24 ] reported increased levels of the presence of meaning in life following a meditation course comprising mindfulness practices and cultivation of loving kindness and compassionate attitudes towards oneself and others in a sample of undergraduate college students.

Purpose in life has been proposed to be a central construct to explain well-being- and health-related outcomes [ 15 ]. People engaged in a purposeful and meaningful life tend to report positive outcomes in terms of well-being and mental health. The presence of meaning in life appears to be linked to happiness, life satisfaction, and positive affect [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. Moreover, having purposes in life has been found to be negatively connected with various psychopathological problems [ 34 , 35 ], such as depressive symptoms [ 9 , 36 ], suicidal ideation [ 37 , 38 ], anxiety-related responses [ 39 ], post-traumatic stress disorder [ 40 ], social anxiety [ 41 ], and sleep disturbances [ 42 ], among other issues. Higher levels of purpose in life could be even connected to better physical health outcomes, through enhanced functioning of physiological systems [ 23 , 35 ]. Interestingly, previous research has found that meaning in life mediated the relationships between mindfulness and positive and negative wellbeing [ 43 ].

Having purposes in life is somehow considered a motivation to greater behavioral activation. The construct of meaning in life entails a motivational component [ 17 , 18 ], which drives people to attain valuable aims and persevere in significant behavior in spite of potential obstacles [ 15 ]. From a theoretical perspective, purposeful living has been suggested to increase endurance during mentally and physically challenging activities and sustain vitality [ 15 ]. In addition, empirical research has reported strong positive associations between behavioral activation and three components of meaning in life, i.e., comprehension, purpose, and mattering [ 44 ]. Concerning mental health outcomes, interventions focused on increasing behavioral activation have demonstrated large positive effects on depression symptoms [ 45 , 46 , 47 ], shown moderate effects on well-being [ 48 ], and have been suggested as an evidence-based mechanism for anxiety exposure [ 49 ].

Behavioral activation may somewhat explain the relationship between purpose in life and potential mental health and well-being-related outcomes. First, life purposes operate as a motivational component [ 18 ]. Individuals striving to approach and achieve life aims seem to engage in significant behavior [ 15 ]. Second, positive correlations between mindfulness and behavioral activation have been reported [ 50 , 51 , 52 ]. Moreover, Kearney et al. [ 53 , 54 ] found that posttraumatic stress disorder (PTSD) patients who completed a mindfulness-based stress reduction (MBSR) intervention increased their levels of behavioral activation. Interestingly, they showed that changes in mindfulness scores were associated with change in behavioral activation [ 53 ]. Similarly, Gaudiano et al. [ 55 ] reported a strong correlation between changes in mindfulness and changes in behavioral activation, following an acceptance-based depression and psychosis therapy (ADAPT) intervention in patients with major depressive disorder with psychotic features.

1.2. The Present Study

On the basis of previous literature on mindfulness, purpose in life, behavioral activation, and well-being and mental health-related outcomes, our study aimed to test a model that integrates the complete patterns of relationships among these variables. This model entails the following hypotheses:

Mindfulness is expected to be positively correlated with happiness (H1.1) and negatively associated with anxiety (H1.2) and depression (H1.3) measures.

Higher levels of mindfulness will be connected with a greater sense of purpose in life (H2.1) and behavioral activation (H2.2).

Purpose in life will be positively connected with happiness (H3.1) and negatively associated with anxiety (H3.2) and depression (H3.3) measures.

Higher behavioral activation is expected to be associated with higher levels of happiness (H4.1) and lower scores in anxiety (H4.2) and depression (H4.3).

Higher levels of purpose in life are expected to correspond to higher levels of behavioral activation (H5).

Taken as a whole, the abovementioned hypotheses suggest some possible mediating effects that will be tested:

The associations between mindfulness and happiness (H6.1), anxiety (H6.2), and depression (H6.3) may be accounted for, to some extent, by both purpose in life and behavioral activation.

The link between mindfulness and behavioral activation may be accounted for, to some extent, by purpose in life (H7).

The associations between purpose in life and happiness (H8.1), anxiety (H8.2), and depression (H8.3) may be accounted for, to some extent, by behavioral activation.

2. Materials and Methods

2.1. participants.

Our sample comprised 1267 women. Their mean age was 33.76 years ( SD = 14.85) and ranged from 18 to 70 years. Eighteen Latin American Spanish-speaking countries and Spain were represented. Most of the participants came from Venezuela (44.2%), Nicaragua (10.3%), Bolivia (8.4%), Paraguay (5.6%), the Dominican Republic (5.4%), and Argentina (5.2%). Other nationalities represented less than 5%. Concerning education, 14% of the participants reached the postgraduate level, and 52.2% completed graduate studies. Professional training and high-school education represented, respectively, 15.4% and 16.3%, with elementary studies being reported by 1.9% of the sample. More than one-third of the participants (38.8%) were active workers, 14.1% were unemployed, 32% were students without a job allowing for economic autonomy, 7.7% were retirees, and 7.3% reported other labor situations. With respect to the participants’ attitudes toward religion, most (56.2%) characterized themselves as “nonpracticing believers”; 27.7% reported being believers involved in religious practice; and 16.1% defined themselves as atheists, agnostics, or indifferent concerning religious belief.

2.2. Procedure

An online questionnaire was used to collect data from May to July 2018. The respondents were informed that this study was part of a research project aiming to know, from a psychological perspective, more about meaning in life, mindful living, health, and well-being. The questionnaire did not request any data allowing for individual identification of participants. Before starting the survey, the respondents were also informed that all data would be anonymous. A snowball method was used to distribute questionnaires through online social networks, encouraging participants to share the link to the survey webpage among their acquaintances.

Completing the questionnaire was voluntary, with no monetary or material compensation or other incentive for participants. Informed consent was obtained for all participants. All procedures performed in this study were done in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments. This research received approval from the Research Ethics Committee of the Pontifical University of Salamanca (Minutes of the meeting 17 July 2018). In total, 1528 questionnaires were received. However, 155 of them were discarded because they were duplicated submissions (57 questionnaires) or responses out of the age range of this research, i.e., younger than 18 years (82 questionnaires) or older than 70 years (16 questionnaires). Questionnaires from male individuals (106 submissions) were not considered in this study in order to avoid potential problems due to the unequal distribution of gender among respondents. Therefore, female respondents participating in this study represent 82.92% of the received questionnaires.

2.3. Instruments

Mindfulness. The participants’ capacity for paying attention to experiences and being fully aware of internal and external stimuli while being focused in the present moment was measured by means of the Mindful Attention Awareness Scale (MAAS), developed by Brown and Ryan [ 56 ] (Spanish adaptation by Soler et al. [ 57 ]). This scale comprises 15 items designed to measure the individual’s general mindfulness capacity. It uses a 6-point Likert-type response format where 1 = “Almost always” and 6 = “Almost never”. An example item is “I find it difficult to stay focused on what’s happening in the present”. Total scores were obtained by averaging each participant’s responses to the items, with higher scores meaning a greater mindfulness capacity. Internal consistency was α = 0.88.

Purpose in life. The Spanish version, developed by Díaz et al. [ 58 ], of the Purpose in Life Scale included in the Ryff’s Psychological Well-being Scales was used [ 59 ]. This 5-item scale aims to assess how much the respondents feel their lives are purposeful and meaningful. An example item is “I have a sense of direction and purpose in life”. Participants responded on a 6-point Likert-type scale, where 1 = “Totally disagree” and 6 = “Totally agree”. A total score for each participant was calculated by averaging the 5 items of the subscale, with higher scores representing higher levels of purpose in life. The internal consistency reliability for the purpose in life items was α = 0.87.

Behavioral activation. The 7-item “Activation” subscale of the Behavioral Activation for Depression Scale (BADS), developed by Kanter et al. [ 60 ] (Spanish adaptation by Barraca et al. [ 61 ]) was used. These items measure focused, goal-directed activation and completion of planned activities, e.g., “I did things even though they were hard because they fit in with my long-term goals for myself”. Responses are made on a 7-point Likert-type scale from 0 = “Not at all” to 6 = “Completely”. Items were averaged to obtain a total score for each participant. Higher scores reflect a higher level of behavioral activation. Internal consistency was α = 0.92.

The Subjective Happiness Scale (SHS) is a 4-item scale used to measure the global level of perceived happiness [ 62 ]; Spanish translation of items and wording of response labels have been done by Extremera and Fernández-Berrocal [ 63 ]. Two items ask respondents to report the extent to which they consider themselves to be a happy or not-happy person, in absolute terms and relative to other people (e.g., “Compared to most of my peers, I consider myself: less happy/more happy”). The other two items present descriptions of happy and unhappy people, and respondents are requested to indicate the extent to which each description applies to themselves (e.g., “Some people are generally very happy. They enjoy life regardless of what is going on, getting the most out of everything. To what extent does this characterization describe you?: not at all/a great deal”). All items use a 7-point Likert-type scale. The total scores of subjective happiness were calculated for each participant by averaging responses to the 4 items (range 1–7), with higher scores indicating greater subjective happiness. The internal consistency reliability was α = 0.85.

Anxiety and depression symptoms. The Hospital Anxiety and Depression Scale (HAD), developed by Zigmond and Snaith, was used [ 64 ] (Spanish adaptation by Terol et al. [ 65 ]). This scale comprises 14 items intended as a screening instrument to detect possible anxiety (7 items) and depression (7 items problems). Examples items are “I get sudden feelings of panic” (anxiety) and “I have lost interest in my appearance” (depression). Participants respond by selecting 1 of 4 alternatives that are scored from 0 to 3. Scores for anxiety and depression are calculated by adding the individual’s responses to the items in each subscale, with higher scores indicating higher levels of anxiety and depression. The internal consistency values were α = 0.85 (anxiety subscale) and α = 0.76 (depression subscale).

2.4. Data Analyses

Descriptive statistics (means and standard deviations) were calculated for the study variables. Pearson’s r bivariate correlations were obtained to assess the associations among variables.

Following Hayes [ 66 , 67 ], bootstrap confidence intervals are preferred to normal theory tests for inference about indirect effects (i.e., the mediation effects). Therefore, a bootstrapping-based method was used to test mediation effects. Point estimates and 95% bias-corrected ( BC ) bootstrap confidence intervals (percentile method) for the indirect (mediated) effects were calculated using 5000 bootstrap samples. A statistically significant mediated effect, different from zero with 95% confidence, is obtained if zero is not between the lower and upper bound of the BC confidence interval for the indirect effects. The ratio of the indirect effect to the total effect ( P M ) was calculated as an effect-size measure for mediation effects. Although this ratio cannot be properly interpreted in terms of the proportion of the explained variance in total effects that is due to the indirect effect [ 68 ], it is usually reported as an index of the relative magnitude of the mediation path.

Our hypotheses entailed 2 mediators, purpose in life ( M 1 ) and behavioral activation ( M 2 ), and 3 outcome variables: happiness ( Y 1 ), anxiety ( Y 2 ), and depression ( Y 3 ). Therefore, a multiple-step multiple mediator model was used [ 66 , 67 ] ( Figure 1 ). A structural equation modeling (SEM)-based approach was used to test the hypothesized effects. SEM modeling allows for testing mediation effects simultaneously, with the advantage of identifying whether a particular mediation is independent of the effect of the other mediators [ 69 ]. Because the mediation models are saturated (i.e., zero degrees of freedom), the Akaike Information Criteria (AIC) was used to compare the adequacy of the proposed model against alternative models where a , b , and/or c’ paths were omitted [ 70 ].

Hypothesized multiple-step multiple mediator model.

Prior to conducting the analyses, quantitative variables were standardized to avoid possible multicollinearity problems because correlations among our study variables were expected. With the values of the variance inflation factor (VIF) below 10, tolerance values higher than 0.2, and condition indexes clearly below 15, allowed us to safely conclude that there was no collinearity in the data, according to the usual criteria [ 71 , 72 ].

All analyses were carried out using the statistical package IBM SPSS 19 and AMOS 16 (IBM, Armonk, NY, USA).

Participants presented moderate levels of mindfulness, with average scores around the midpoint of the response scale ( Table 1 ). Similarly, they reported a moderate presence of purpose in life and behavioral activation. Concerning anxiety scores, overall, participants obtained an average score of 9.19 ( SD = 4.36), which yielded a 95% CI with 8.95 and 9.43 as lower and upper limits, respectively. According to Bjelland et al. [ 73 ], a cut-off point ≥8 may be indicative of anxiety and depression symptoms as measured by the corresponding HAD subscales. Therefore, on average, respondents presented moderate or borderline anxiety-related symptoms. Depression scores were rather low, and clearly below the 8 point cut-off. Finally, given the possible range of scores on the scale, an intermediate average level of subjective happiness was observed among participants.

Descriptive statistics for mindfulness, the proposed mediators (purpose in life and behavior activation), and outcome variables (anxiety, depression, and happiness).

Age was moderately correlated with all of the focus variables of interest, yielding positive associations with mindfulness, purpose in life, behavior activation, and happiness, as well as presenting negative relationships with anxiety and depression ( Table 2 ).

Bivariate correlations among the study variables.

Note: All correlations were significant at the p < 0.001 level (two-tailed).

As presented in Table 2 , the results revealed strong bivariate correlations among the main study variables. Mindfulness was strongly and positively connected to higher happiness (H1.1) and negatively associated with anxiety (H1.2) and depression (H1.3), with percentages of shared variances of 22.09%, 23.04%, and 19.36%, respectively. Higher levels of mindfulness corresponded to greater perceptions of purpose in life (H2.1) and behavioral activation (H2.2) with 21.16% and 15.21% of shared variance between these variables and mindfulness, respectively. In addition, experiencing a purposeful life was linked to positive outcomes, such as increased happiness (H3.1) and reduced anxiety (H3.2) and depression (H3.3) symptoms, with percentages of shared variance ranging from 46.24% to 25%. Behavioral activation was also strongly associated with the outcome variables. The higher the behavioral activation reported, the higher the reported happiness (H4.1) scores and the lower the symptoms-related scores (H4.2 and H4.3). The percentage of shared variance between happiness and behavioral activation was 31.36%, and depression and anxiety yielded percentages of 32.49% and 20.25%, respectively, of shared variations with behavioral activation. Participants reporting a highly meaningful and purposeful life also reported higher levels of completion of planned and goal-directed behavior (H5), with 50.41% of shared variance between purpose in life and behavioral activation.

Finally, as expected, happiness was negatively and statistically significantly associated with anxiety and depression, whereas significantly positive correlations between psychological symptoms were observed.

3.1. Purpose in Life and Behavioral Activation as Mediators of the Associations between Mindfulness and Outcome Variables

After controlling for the effects of age, mindfulness scores significantly predicted happiness, anxiety, and depression, thus confirming the existence of significant total effects that may be mediated ( Table 3 ). Mindfulness was also a significant predictor of the two proposed mediators, with significant total effects on purpose in life and behavioral activation, after controlling for age ( Table 3 ).

Standardized total effects of mindfulness, purpose in life, and behavior activation on mediators and outcome variables.

The proposed mediators also yielded significant total effects on the outcome variables ( Table 3 ). After controlling for age and mindfulness, purpose in life was a significant predictor of happiness, anxiety, and depression. Similarly, behavioral activation predicted happiness, anxiety, and depression, after controlling for age, mindfulness, and purpose in life.

Bootstrap-corrected confidence intervals for the indirect effects of mindfulness through purpose in life and behavioral activation confirmed significant mediation effects on happiness (H6.1), anxiety (H6.2), and depression (H6.3) ( Table 4 ).

Standardized indirect effects of mindfulness and purpose in life on mediators and outcome variables.

3.2. Purpose in Life as a Mediator of the Relationship between Mindfulness and Behavioral Activation

The abovementioned significant connection between mindfulness and behavioral activation was proposed to be mediated through purpose in life (H7). Here, two additional conditions were met. As presented above, mindfulness was also associated with purpose in life. Second, purpose in life yielded a significant total effect on behavioral activation ( Table 3 ), after controlling for age and mindfulness. The bootstrap bias-corrected 95% confidence interval for the indirect effect confirmed a significant mediation effect of purpose in life on the relationship between mindfulness and behavioral activation ( Table 4 ).

3.3. Behavioral Activation as a Mediator of the Associations between Purpose in Life and Outcome Variables

As previously mentioned, purpose in life was a significant predictor of happiness, anxiety, and depression after controlling for age and mindfulness. This relationship was hypothesized to be mediated by behavioral activation. The required conditions were met because purpose in life was a significant predictor of behavioral activation after controlling for age and mindfulness; additionally, behavioral activation significantly predicted the outcome variables (i.e., happiness, anxiety, and depression), after controlling for age, mindfulness, and purpose in life. Bootstrap bias-corrected 95% confidence intervals indicated a significant indirect (mediated) effect of purpose in life on the outcome variables through behavioral activation ( Table 4 ), thus supporting H8.1, H8.2, and H8.3.

The saturated SEM model of the multiple-step multiple mediation ( Figure 2 ) always fit better (AIC = 70.00) than alternative models where paths a , b , or c’ were omitted.

Multiple-step multiple mediator model representing the associations between mindfulness and happiness, anxiety, and depression, with purpose in life and behavior as mediators. Notes: The numbers represent point estimates of direct effects. Dashed lines represent mediated paths. ** p < 0.01; *** p < 0.001. Control variable (i.e., age) is not depicted to provide a clear representation of the model.

With respect to the amount of variance in the outcomes variables that were accounted for by the multiple-step multiple mediator model, 50%, 34%, and 44% of variations in happiness, anxiety, and depression, respectively, were explained.

4. Discussion

Our study contributes to shedding light on the potential mechanisms that may connect mindfulness and healthy outcomes. We found that individuals who reported higher levels of mindfulness also showed higher levels of happiness and lower anxiety and depression symptoms. In this regard, the negative correlations found between mindfulness and depression and anxiety are highly consistent with correlations previously found [ 4 , 56 , 74 ]. Associations between mindfulness and happiness have also been previously reported [ 5 , 74 ].

How is mindfulness connected with positive outcomes? We hypothesized a series of sequential associations between mindfulness, having purposes in life, and increased behavioral activation. In this regard, mindfulness may lead people to clarify values around which to organize their lives and gain awareness of those things that truly matter in life, i.e., to discern their purposes and find meaning in life. Consistent with previous research, we found that individuals reporting higher levels of mindfulness also reported a greater sense of purpose in life [ 9 , 43 , 75 ]. However, why might mindfulness stimulate a person to set life aims? Some new hypotheses may be suggested. First, enhanced awareness of difficult thoughts and painful emotions (i.e., mindfulness), paralleled by a benevolent and nonjudgmental attitude towards oneself, may favor wise discernment, identification of personal values, and disclosure of paths to behavioral activation and persistence in the face of adverse events. As mentioned, previous research has already linked mindfulness skills and life purpose assessments (e.g., Allan et al. [ 19 ]). Second, mindfulness-related processes and attitudes, such as decentering, acceptance, openness, and not judging, could reduce the extent to which individuals are exposed to self-punishment, blame, shame, and other self-directed negative emotions, which could lead to greater appreciation of life and savoring of things that matter, i.e., increasing meaningfulness.

Purpose in life and the outcome variables were connected as expected. The association between having purposes and happiness was coincident with Lyubomirsky et al. [ 76 ] and García-Alandete [ 26 ], as well as being consistent with correlations between meaning in life and happiness reported by Steger et al. [ 33 ] and Schueller and Seligman [ 77 ]. The association found between purpose and depression was also similar to that reported in previous research [ 33 , 78 , 79 ]. Concerning anxiety, a similar correlation to ours was reported by Ishida and Okada [ 80 ]. Our results concerning the relationships between purpose in life and depression and anxiety are also consistent with those of Ho et al. [ 81 ], Schnell [ 31 ], Scheier et al. [ 82 ], and Pearson et al. [ 9 ], although lower correlations were obtained by these authors.

Interestingly, Pearson et al. [ 9 ] tested a path model where purpose in life was proposed to mediate the relationships among trait mindfulness and anxiety and depression symptoms. They found that purpose in life actually mediated the association between mindfulness and depression. However, purpose in life had no significant direct effect on anxiety symptoms, as another mediator, i.e., decentering, appeared to better account for the association between mindfulness and anxiety. These results may indicate that, while purpose in life is highly relevant to explain mood-related and well-being related outcomes, it may play a different role concerning anxiety symptoms. In our research, we found significant associations (as measured by correlations and total effects) between purpose in life and anxiety scores. However, in terms of effect size and percentage of explained variance, our model also revealed that the associations of purpose in life with happiness and depression were stronger than the connection between having purposes and anxiety.

This research also contributes to extend previous models where meaningfulness is considered to mediate the positive effects of mindfulness [ 43 ] by adding a new variable, i.e., behavioral activation. We hypothesized that mindfulness will be connected with the individual’s activation, a relationship that could be partially explained by the links between purposefulness with both mindfulness and behavioral activation. In this regard, the positive correlation found between mindfulness and behavioral activation was coherent with previous findings [ 50 , 51 , 52 ]. Furthermore, our results indicate that such connection between mindfulness and activation may be explained, to a great extent, by higher levels of purposefulness. The more a person perceives his or her life has a purpose and envisions valuable aims to pursue, the more prone to engage with activities he or she will be. Previous research has already found a positive connection between purpose in life and behavioral activation [ 44 ], which is consistent with our results. As presented, purpose in life is assumed to play a motivational role that prompt exposure to experience [ 7 , 15 , 18 ]. Finally, our results concerning the association of behavioral activation with anxiety, depression, and happiness are also aligned with previous studies [ 47 , 48 , 52 , 83 ].

In summary, higher levels of mindfulness and purpose in life appears to be connected with greater engagement with life, which is linked to beneficial psychological outcomes.

4.1. Limitations and Future Research

This research’s contributions should be considered in the light of its limitations. First, although relationships among variables depicted in SEM models apparently suggest causality, especially when mediation analyses are tested, such causal inferences cannot be properly derived from cross-sectional data, as used in this research. Therefore, assertions that may suggest a possible direction of causality should be taken cautiously. In this regard, longitudinal designs and cross-lagged analyses would be advisable in future research. Research exploring the effects of mindfulness-based interventions would offer interesting possibilities to longitudinally analyze whether changes in mindfulness-related attitudes may lead to changes in meaningfulness and changes in engagement with life, which would be expected to be associated with changes in wellbeing and mental health across time. A second limitation comes from the sample participating in this study. As stated, we used a convenience sample, recruited among internet users (i.e., the survey was distributed online), which may compromise the generalization of the results. In particular, our study used a sample of women. Previous research has found that women usually report higher levels of anxiety [ 84 ] and depression [ 85 ]. Although a comparatively small number of men responded to the online survey, their data were not used in the analyses to avoid potential gender biases in the analyses performed, due to the imbalance that would have occurred in the overall sample. While this is an obvious limitation, several previous studies have also pointed out the unequal participation rates for males and females in online surveys [ 86 , 87 ]. Despite this limitation, our results concerning the relationships among variables were highly consistent with findings from previous research. In any case, a possible line of future research would be analyzing the influence of gender on the relationships among mindfulness, purpose in life, behavioral activation, mental health and happiness, and testing of whether the proposed model is also applicable to the male population. Previous literature has also suggested the convenience of paying attention to gender in the context of mindfulness research [ 88 ]. Likewise, an in-depth analysis of the validity of the model in different age groups would also be interesting.

Finally, we considered two mediating mechanisms that, along with mindfulness, could account for the great proportions of the variance of happiness, anxiety, and depression. However, our mediating variables do not exhaust other potential explanatory mechanisms that may be involved in the relationship between mindfulness and psychological outcomes. Similarly, additional mediating and/or moderating variables could be explored concerning the relationships among mindfulness and purpose in life and the connections between purpose and behavioral activation. In this regard, research focused on integrating new possible explanatory variables into the proposed model would be worth doing. For instance, it would be interesting to analyze the role played by personality traits, and eventually include this variable in the model, since previous research has found that part of the variance in meaningfulness can be explained by personality factors [ 89 ]. In addition, it would be also interesting to know how contextual factors may affect the relationships between variables. Our data collection was conducted prior to the emergence of the global coronavirus disease 2019 (COVID-19) pandemic, which has been a major stressor for many people. A global event of this magnitude may have prompted many individuals to reflect on their life purposes and identify values that make life meaningful. On the other hand, the lockdowns occurring in many countries may have represented a challenge to maintaining adequate levels of behavioral activation. For these reasons, it would be interesting to test how robust the proposed model is in exceptional situations, such as those experienced as a result of the COVID-19 pandemic.

4.2. Practical Implications

Our results may contribute to provide some rationale for intervention approaches that include the training of mindfulness skills. As presented, mindfulness is associated with positive psychological outcomes. Moreover, this study contributes to shed light on how such association may be accounted for. Having a sense of purpose in life and behavioral activation appeared to play a relevant role on the connections between mindfulness and happiness, anxiety, and depression. This point yields important possibilities for clinical practice. For instance, clinicians using mindfulness-based approaches could presumably strengthen the links between mindfulness and positive outcomes by focusing on promoting a sense of purpose and fostering involvement in meaningful activities, as such variables are associated to lower anxiety and depression symptoms and higher happiness. In fact, evidence-based approaches such as acceptance and commitment therapy [ 90 ] and the mindful self-compassion protocol [ 91 ] already include, in addition to working on mindfulness skills, sessions aimed at promoting meaningful purposes and engagement in actions that are valuable for the individual.

5. Conclusions

Mindfulness, meaningfulness, and behavioral activation are connected with enhanced happiness and reduced anxiety and depression symptoms. Moreover, mindfulness is linked to increased sense of purpose in life, which was revealed to be associated with greater activation and engagement with valued activities. To some extent, the relationships between mindfulness and salutary outcomes could be accounted for by factors such as meaningfulness and behavioral activation, which were identified as mediating variables. Here, the findings may contribute to suggest psychological pathways aiming to reduce mental health disturbances and promote a happy life.

Author Contributions

Conceptualization, A.C., J.R.Y., and M.Á.G.-M.; methodology, A.C. and J.R.Y.; software, P.R.-M.; validation, C.P.-R.; formal analysis, A.C., J.R.Y., and M.Á.G.-M.; investigation, A.C., J.R.Y., and M.Á.G.-M.; resources, A.C. and J.R.Y.; data curation, A.C.; writing—original draft preparation, A.C. and J.R.Y.; writing—review and editing, M.Á.G.-M., P.R.-M., and C.P.-R.; visualization, P.R.-M. and C.P.-R.; supervision, A.C., J.R.Y., M.Á.G.-M., P.R.-M., and C.P.-R.; project administration, J.R.Y. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of the Pontifical University of Salamanca (Minutes of the meeting held on 17 July 2018).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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