mental well being research paper

• Open access
• 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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ORIGINAL RESEARCH article

Academic stress and mental well-being in college students: correlations, affected groups, and covid-19.

• 1 Department of Neurology, Rutgers New Jersey Medical School, Newark, NJ, United States
• 2 Rutgers New Jersey Medical School, Newark, NJ, United States
• 3 Office for Diversity and Community Engagement, Rutgers New Jersey Medical School, Newark, NJ, United States
• 4 Department of Biology, The College of New Jersey, Ewing, NJ, United States

Academic stress may be the single most dominant stress factor that affects the mental well-being of college students. Some groups of students may experience more stress than others, and the coronavirus disease 19 (COVID-19) pandemic could further complicate the stress response. We surveyed 843 college students and evaluated whether academic stress levels affected their mental health, and if so, whether there were specific vulnerable groups by gender, race/ethnicity, year of study, and reaction to the pandemic. Using a combination of scores from the Perception of Academic Stress Scale (PAS) and the Short Warwick-Edinburgh Mental Well-Being Scale (SWEMWBS), we found a significant correlation between worse academic stress and poor mental well-being in all the students, who also reported an exacerbation of stress in response to the pandemic. In addition, SWEMWBS scores revealed the lowest mental health and highest academic stress in non-binary individuals, and the opposite trend was observed for both the measures in men. Furthermore, women and non-binary students reported higher academic stress than men, as indicated by PAS scores. The same pattern held as a reaction to COVID-19-related stress. PAS scores and responses to the pandemic varied by the year of study, but no obvious patterns emerged. These results indicate that academic stress in college is significantly correlated to psychological well-being in the students who responded to this survey. In addition, some groups of college students are more affected by stress than others, and additional resources and support should be provided to them.

Introduction

Late adolescence and emerging adulthood are transitional periods marked by major physiological and psychological changes, including elevated stress ( Hogan and Astone, 1986 ; Arnett, 2000 ; Shanahan, 2000 ; Spear, 2000 ; Scales et al., 2015 ; Romeo et al., 2016 ; Barbayannis et al., 2017 ; Chiang et al., 2019 ; Lally and Valentine-French, 2019 ; Matud et al., 2020 ). This pattern is particularly true for college students. According to a 2015 American College Health Association-National College Health Assessment survey, three in four college students self-reported feeling stressed, while one in five college students reported stress-related suicidal ideation ( Liu, C. H., et al., 2019 ; American Psychological Association, 2020 ). Studies show that a stressor experienced in college may serve as a predictor of mental health diagnoses ( Pedrelli et al., 2015 ; Liu, C. H., et al., 2019 ; Karyotaki et al., 2020 ). Indeed, many mental health disorders, including depression, anxiety, and substance abuse disorder, begin during this period ( Blanco et al., 2008 ; Pedrelli et al., 2015 ; Saleh et al., 2017 ; Reddy et al., 2018 ; Liu, C. H., et al., 2019 ).

Stress experienced by college students is multi-factorial and can be attributed to a variety of contributing factors ( Reddy et al., 2018 ; Karyotaki et al., 2020 ). A growing body of evidence suggests that academic-related stress plays a significant role in college ( Misra and McKean, 2000 ; Dusselier et al., 2005 ; Elias et al., 2011 ; Bedewy and Gabriel, 2015 ; Hj Ramli et al., 2018 ; Reddy et al., 2018 ; Pascoe et al., 2020 ). For instance, as many as 87% of college students surveyed across the United States cited education as their primary source of stress ( American Psychological Association, 2020 ). College students are exposed to novel academic stressors, such as an extensive academic course load, substantial studying, time management, classroom competition, financial concerns, familial pressures, and adapting to a new environment ( Misra and Castillo, 2004 ; Byrd and McKinney, 2012 ; Ekpenyong et al., 2013 ; Bedewy and Gabriel, 2015 ; Ketchen Lipson et al., 2015 ; Pedrelli et al., 2015 ; Reddy et al., 2018 ; Liu, C. H., et al., 2019 ; Freire et al., 2020 ; Karyotaki et al., 2020 ). Academic stress can reduce motivation, hinder academic achievement, and lead to increased college dropout rates ( Pascoe et al., 2020 ).

Academic stress has also been shown to negatively impact mental health in students ( Li and Lin, 2003 ; Eisenberg et al., 2009 ; Green et al., 2021 ). Mental, or psychological, well-being is one of the components of positive mental health, and it includes happiness, life satisfaction, stress management, and psychological functioning ( Ryan and Deci, 2001 ; Tennant et al., 2007 ; Galderisi et al., 2015 ; Trout and Alsandor, 2020 ; Defeyter et al., 2021 ; Green et al., 2021 ). Positive mental health is an understudied but important area that helps paint a more comprehensive picture of overall mental health ( Tennant et al., 2007 ; Margraf et al., 2020 ). Moreover, positive mental health has been shown to be predictive of both negative and positive mental health indicators over time ( Margraf et al., 2020 ). Further exploring the relationship between academic stress and mental well-being is important because poor mental well-being has been shown to affect academic performance in college ( Tennant et al., 2007 ; Eisenberg et al., 2009 ; Freire et al., 2016 ).

Perception of academic stress varies among different groups of college students ( Lee et al., 2021 ). For instance, female college students report experiencing increased stress than their male counterparts ( Misra et al., 2000 ; Eisenberg et al., 2007 ; Evans et al., 2018 ; Lee et al., 2021 ). Male and female students also respond differently to stressors ( Misra et al., 2000 ; Verma et al., 2011 ). Moreover, compared to their cisgender peers, non-binary students report increased stressors and mental health issues ( Budge et al., 2020 ). The academic year of study of the college students has also been shown to impact academic stress levels ( Misra and McKean, 2000 ; Elias et al., 2011 ; Wyatt et al., 2017 ; Liu, C. H., et al., 2019 ; Defeyter et al., 2021 ). While several studies indicate that racial/ethnic minority groups of students, including Black/African American, Hispanic/Latino, and Asian American students, are more likely to experience anxiety, depression, and suicidality than their white peers ( Lesure-Lester and King, 2004 ; Lipson et al., 2018 ; Liu, C. H., et al., 2019 ; Kodish et al., 2022 ), these studies are limited and often report mixed or inconclusive findings ( Liu, C. H., et al., 2019 ; Kodish et al., 2022 ). Therefore, more studies should be conducted to address this gap in research to help identify subgroups that may be disproportionately impacted by academic stress and lower well-being.

The coronavirus disease 19 (COVID-19) pandemic is a major stressor that has led to a mental health crisis ( American Psychological Association, 2020 ; Dong and Bouey, 2020 ). For college students, the COVID-19 pandemic has resulted in significant changes and disruptions to daily life, elevated stress levels, and mental and physical health deterioration ( American Psychological Association, 2020 ; Husky et al., 2020 ; Patsali et al., 2020 ; Son et al., 2020 ; Clabaugh et al., 2021 ; Lee et al., 2021 ; Lopes and Nihei, 2021 ; Yang et al., 2021 ). While any college student is vulnerable to these stressors, these concerns are amplified for members of minority groups ( Salerno et al., 2020 ; Clabaugh et al., 2021 ; McQuaid et al., 2021 ; Prowse et al., 2021 ; Kodish et al., 2022 ). Identifying students at greatest risk provides opportunities to offer support, resources, and mental health services to specific subgroups.

The overall aim of this study was to assess academic stress and mental well-being in a sample of college students. Within this umbrella, we had several goals. First, to determine whether a relationship exists between the two constructs of perceived academic stress, measured by the Perception of Academic Stress Scale (PAS), and mental well-being, measured by the Short Warwick-Edinburgh Mental Well-Being Scale (SWEMWBS), in college students. Second, to identify groups that could experience differential levels of academic stress and mental health. Third, to explore how the perception of the ongoing COVID-19 pandemic affected stress levels. We hypothesized that students who experienced more academic stress would have worse psychological well-being and that certain groups of students would be more impacted by academic- and COVID-19-related stress.

Materials and Methods

Survey instrument.

A survey was developed that included all questions from the Short Warwick-Edinburgh Mental Well-Being ( Tennant et al., 2007 ; Stewart-Brown and Janmohamed, 2008 ) and from the Perception of Academic Stress Scale ( Bedewy and Gabriel, 2015 ). The Short Warwick-Edinburgh Mental Well-Being Scale is a seven-item scale designed to measure mental well-being and positive mental health ( Tennant et al., 2007 ; Fung, 2019 ; Shah et al., 2021 ). The Perception of Academic Stress Scale is an 18-item scale designed to assess sources of academic stress perceived by individuals and measures three main academic stressors: academic expectations, workload and examinations, and academic self-perceptions of students ( Bedewy and Gabriel, 2015 ). These shorter scales were chosen to increase our response and study completion rates ( Kost and de Rosa, 2018 ). Both tools have been shown to be valid and reliable in college students with Likert scale responses ( Tennant et al., 2007 ; Bedewy and Gabriel, 2015 ; Ringdal et al., 2018 ; Fung, 2019 ; Koushede et al., 2019 ). Both the SWEMWBS and PAS scores are a summation of responses to the individual questions in the instruments. For the SWEMWBS questions, a higher score indicates better mental health, and scores range from 7 to 35. Similarly, the PAS questions are phrased such that a higher score indicates lower levels of stress, and scores range from 18 to 90. We augmented the survey with demographic questions (e.g., age, gender, and race/ethnicity) at the beginning of the survey and two yes/no questions and one Likert scale question about the impact of the COVID-19 pandemic at the end of our survey.

Participants for the study were self-reported college students between the ages of 18 and 30 years who resided in the United States, were fluent in English, and had Internet access. Participants were solicited through Prolific ( https://prolific.co ) in October 2021. A total of 1,023 individuals enrolled in the survey. Three individuals did not agree to participate after beginning the survey. Two were not fluent in English. Thirteen individuals indicated that they were not college students. Two were not in the 18–30 age range, and one was located outside of the United States. Of the remaining individuals, 906 were full-time students and 96 were part-time students. Given the skew of the data and potential differences in these populations, we removed the part-time students. Of the 906 full-time students, 58 indicated that they were in their fifth year of college or higher. We understand that not every student completes their undergraduate studies in 4 years, but we did not want to have a mixture of undergraduate and graduate students with no way to differentiate them. Finally, one individual reported their age as a non-number, and four individuals did not answer a question about their response to the COVID-19 pandemic. This yielded a final sample of 843 college students.

Data Analyses

After reviewing the dataset, some variables were removed from consideration due to a lack of consistency (e.g., some students reported annual income for themselves and others reported family income) or heterogeneity that prevented easy categorization (e.g., field of study). We settled on four variables of interest: gender, race/ethnicity, year in school, and response to the COVID-19 pandemic ( Table 1 ). Gender was coded as female, male, or non-binary. Race/ethnicity was coded as white or Caucasian; Black or African American; East Asian; Hispanic, Latino, or of Spanish origin; or other. Other was used for groups that were not well-represented in the sample and included individuals who identified themselves as Middle Eastern, Native American or Alaskan Native, and South Asian, as well as individuals who chose “other” or “prefer not to answer” on the survey. The year of study was coded as one through four, and COVID-19 stress was coded as two groups, no change/neutral response/reduced stress or increased stress.

Table 1 . Characteristics of the participants in the study.

Our first goal was to determine whether there was a relationship between self-reported academic stress and mental health, and we found a significant correlation (see Results section). Given the positive correlation, a multivariate analysis of variance (MANOVA) with a model testing the main effects of gender, race/ethnicity, and year of study was run in SPSS v 26.0. A factorial MANOVA would have been ideal, but our data were drawn from a convenience sample, which did not give equal representation to all groupings, and some combinations of gender, race/ethnicity, and year of study were poorly represented (e.g., a single individual). As such, we determined that it would be better to have a lack of interaction terms as a limitation to the study than to provide potentially spurious results. Finally, we used chi-square analyses to assess the effect of potential differences in the perception of the COVID-19 pandemic on stress levels in general among the groups in each category (gender, race/ethnicity, and year of study).

In terms of internal consistency, Cronbach's alpha was 0.82 for the SMEMWBS and 0.86 for the PAS. A variety of descriptors have been applied to Cronbach's alpha values. That said, 0.7 is often considered a threshold value in terms of acceptable internal consistency, and our values could be considered “high” or “good” ( Taber, 2018 ).

The participants in our study were primarily women (78.5% of respondents; Table 1 ). Participants were not equally distributed among races/ethnicities, with the majority of students selecting white or Caucasian (66.4% of responders; Table 1 ), or years of study, with fewer first-year students than other groups ( Table 1 ).

Students who reported higher academic stress also reported worse mental well-being in general, irrespective of age, gender, race/ethnicity, or year of study. PAS and SWEMWBS scores were significantly correlated ( r = 0.53, p < 0.001; Figure 1 ), indicating that a higher level of perceived academic stress is associated with worse mental well-being in college students within the United States.

Figure 1 . SWEMWBS and PAS scores for all participants.

Among the subgroups of students, women, non-binary students, and second-year students reported higher academic stress levels and worse mental well-being ( Table 2 ; Figures 2 – 4 ). In addition, the combined measures differed significantly between the groups in each category ( Table 2 ). However, as measured by partial eta squared, the effect sizes were relatively small, given the convention of 0.01 = small, 0.06 = medium, and 0.14 = large differences ( Lakens, 2013 ). As such, there were only two instances in which Tukey's post-hoc tests revealed more than one statistical grouping ( Figures 2 – 4 ). For SWEMWBS score by gender, women were intermediate between men (high) and non-binary individuals (low) and not significantly different from either group ( Figure 2 ). Second-year students had the lowest PAS scores for the year of study, and first-year students had the highest scores. Third- and fourth-year students were intermediate and not statistically different from the other two groups ( Figure 4 ). There were no pairwise differences in academic stress levels or mental well-being among racial/ethnic groups.

Table 2 . Results of the MANOVA.

Figure 2 . SWEMWBS and PAS scores according to gender (mean ± SEM). Different letters for SWEMWBS scores indicate different statistical groupings ( p < 0.05).

Figure 3 . SWEMWBS and PAS scores according to race/ethnicity (mean ± SEM).

Figure 4 . SWEMWBS and PAS scores according to year in college (mean ± SEM). Different letters for PAS scores indicate different statistical groupings ( p < 0.05).

The findings varied among categories in terms of stress responses due to the COVID-19 pandemic ( Table 3 ). For gender, men were less likely than women or non-binary individuals to report increased stress from COVID-19 (χ 2 = 27.98, df = 2, p < 0.001). All racial/ethnic groups responded similarly to the pandemic (χ 2 = 3.41, df = 4, p < 0.49). For the year of study, first-year students were less likely than other cohorts to report increased stress from COVID-19 (χ 2 = 9.38, df = 3, p < 0.03).

Table 3 . Impact of COVID-19 on stress level by gender, race/ethnicity, and year of study.

Our primary findings showed a positive correlation between perceived academic stress and mental well-being in United States college students, suggesting that academic stressors, including academic expectations, workload and grading, and students' academic self-perceptions, are equally important as psychological well-being. Overall, irrespective of gender, race/ethnicity, or year of study, students who reported higher academic stress levels experienced diminished mental well-being. The utilization of well-established scales and a large sample size are strengths of this study. Our results extend and contribute to the existing literature on stress by confirming findings from past studies that reported higher academic stress and lower psychological well-being in college students utilizing the same two scales ( Green et al., 2021 ; Syed, 2021 ). To our knowledge, the majority of other prior studies with similar findings examined different components of stress, studied negative mental health indicators, used different scales or methods, employed smaller sample sizes, or were conducted in different countries ( Li and Lin, 2003 ; American Psychological Association, 2020 ; Husky et al., 2020 ; Pascoe et al., 2020 ; Patsali et al., 2020 ; Clabaugh et al., 2021 ; Lee et al., 2021 ; Lopes and Nihei, 2021 ; Yang et al., 2021 ).

This study also demonstrated that college students are not uniformly impacted by academic stress or pandemic-related stress and that there are significant group-level differences in mental well-being. Specifically, non-binary individuals and second-year students were disproportionately impacted by academic stress. When considering the effects of gender, non-binary students, in comparison to gender-conforming students, reported the highest stress levels and worst psychological well-being. Although there is a paucity of research examining the impact of academic stress in non-binary college students, prior studies have indicated that non-binary adults face adverse mental health outcomes when compared to male and female-identifying individuals ( Thorne et al., 2018 ; Jones et al., 2019 ; Budge et al., 2020 ). Alarmingly, Lipson et al. (2019) found that gender non-conforming college students were two to four times more likely to experience mental health struggles than cisgender students ( Lipson et al., 2019 ). With a growing number of college students in the United States identifying as as non-binary, additional studies could offer invaluable insight into how academic stress affects this population ( Budge et al., 2020 ).

In addition, we found that second-year students reported the most academic-related distress and lowest psychological well-being relative to students in other years of study. We surmise this may be due to this group taking advanced courses, managing heavier academic workloads, and exploring different majors. Other studies support our findings and suggest higher stress levels could be attributed to increased studying and difficulties with time management, as well as having less well-established social support networks and coping mechanisms compared to upperclassmen ( Allen and Hiebert, 1991 ; Misra and McKean, 2000 ; Liu, X et al., 2019 ). Benefiting from their additional experience, upperclassmen may have developed more sophisticated studying skills, formed peer support groups, and identified approaches to better manage their academic stress ( Allen and Hiebert, 1991 ; Misra and McKean, 2000 ). Our findings suggest that colleges should consider offering tailored mental health resources, such as time management and study skill workshops, based on the year of study to improve students' stress levels and psychological well-being ( Liu, X et al., 2019 ).

Although this study reported no significant differences regarding race or ethnicity, this does not indicate that minority groups experienced less academic stress or better mental well-being ( Lee et al., 2021 ). Instead, our results may reflect the low sample size of non-white races/ethnicities, which may not have given enough statistical power to corroborate. In addition, since coping and resilience are important mediators of subjective stress experiences ( Freire et al., 2020 ), we speculate that the lower ratios of stress reported in non-white participants in our study (75 vs. 81) may be because they are more accustomed to adversity and thereby more resilient ( Brown, 2008 ; Acheampong et al., 2019 ). Furthermore, ethnic minority students may face stigma when reporting mental health struggles ( Liu, C. H., et al., 2019 ; Lee et al., 2021 ). For instance, studies showed that Black/African American, Hispanic/Latino, and Asian American students disclose fewer mental health issues than white students ( Liu, C. H., et al., 2019 ; Lee et al., 2021 ). Moreover, the ability to identify stressors and mental health problems may manifest differently culturally for some minority groups ( Huang and Zane, 2016 ; Liu, C. H., et al., 2019 ). Contrary to our findings, other studies cited racial disparities in academic stress levels and mental well-being of students. More specifically, Negga et al. (2007) concluded that African American college students were more susceptible to higher academic stress levels than their white classmates ( Negga et al., 2007 ). Another study reported that minority students experienced greater distress and worse mental health outcomes compared to non-minority students ( Smith et al., 2014 ). Since there may be racial disparities in access to mental health services at the college level, universities, professors, and counselors should offer additional resources to support these students while closely monitoring their psychological well-being ( Lipson et al., 2018 ; Liu, C. H., et al., 2019 ).

While the COVID-19 pandemic increased stress levels in all the students included in our study, women, non-binary students, and upperclassmen were disproportionately affected. An overwhelming body of evidence suggests that the majority of college students experienced increased stress levels and worsening mental health as a result of the pandemic ( Allen and Hiebert, 1991 ; American Psychological Association, 2020 ; Husky et al., 2020 ; Patsali et al., 2020 ; Son et al., 2020 ; Clabaugh et al., 2021 ; Lee et al., 2021 ; Yang et al., 2021 ). Our results also align with prior studies that found similar subgroups of students experience disproportionate pandemic-related distress ( Gao et al., 2020 ; Clabaugh et al., 2021 ; Hunt et al., 2021 ; Jarrett et al., 2021 ; Lee et al., 2021 ; Chen and Lucock, 2022 ). In particular, the differences between female students and their male peers may be the result of different psychological and physiological responses to stress reactivity, which in turn may contribute to different coping mechanisms to stress and the higher rates of stress-related disorders experienced by women ( Misra et al., 2000 ; Kajantie and Phillips, 2006 ; Verma et al., 2011 ; Gao et al., 2020 ; Graves et al., 2021 ). COVID-19 was a secondary consideration in our study and survey design, so the conclusions drawn here are necessarily limited.

The implications of this study are that college students facing increased stress and struggling with mental health issues should receive personalized and specific mental health services, resources, and support. This is particularly true for groups that have been disproportionately impacted by academic stress and stress due to the pandemic. Many students who experience mental health struggles underutilize college services due to cost, stigma, or lack of information ( Cage et al., 2020 ; Lee et al., 2021 ). To raise awareness and destigmatize mental health, colleges can consider distributing confidential validated assessments, such as the PAS and SWEMWBS, in class and teach students to self-score ( Lee et al., 2021 ). These results can be used to understand how academic stress and mental well-being change over time and allow for specific and targeted interventions for vulnerable groups. In addition, teaching students healthy stress management techniques has been shown to improve psychological well-being ( Alborzkouh et al., 2015 ). Moreover, adaptive coping strategies, including social and emotional support, have been found to improve the mental well-being of students, and stress-reduction peer support groups and workshops on campus could be beneficial in reducing stress and improving the self-efficacy of students ( Ruthig et al., 2009 ; Baqutayan, 2011 ; Bedewy and Gabriel, 2015 ; Freire et al., 2020 ; Green et al., 2021 ; Suresh et al., 2021 ). Other interventions that have been effective in improving the coping skills of college students include cognitive-behavioral therapy, mindfulness mediation, and online coping tools ( Kang et al., 2009 ; Regehr et al., 2013 ; Molla Jafar et al., 2015 ; Phang et al., 2015 ; Houston et al., 2017 ; Yusufov et al., 2019 ; Freire et al., 2020 ). Given that resilience has also been shown to help mediate stress and improve mental well-being during the COVID-19 pandemic, interventions focusing on enhancing resilience should be considered ( Surzykiewicz et al., 2021 ; Skalski et al., 2022 ). Telemental health resources across colleges can also be implemented to reduce stigma and improve at-risk students' access to care ( Toscos et al., 2018 ; Hadler et al., 2021 ). University campuses, professors, and counselors should consider focusing on fostering a more equitable and inclusive environment to encourage marginalized students to seek mental health support ( Budge et al., 2020 ).

Limitations

While our study has numerous strengths, including using standardized instruments and a large sample size, this study also has several limitations due to both the methodology and sample. First, the correlational study design precludes making any causal relationships ( Misra and McKean, 2000 ). Thereby, our findings should be taken in the context of academic stress and mental well-being, and recognize that mental health could be caused by other non-academic factors. Second, the PAS comprised only the perception of responses to academic stress, but stress is a multi-factorial response that encompasses both perceptions and coping mechanisms to different stressors, and the magnitude of stress varies with the perception of the degree of uncontrollability, unpredictability, or threat to self ( Miller, 1981 ; Hobfoll and Walfisch, 1984 ; Lazarus and Folkman, 1984 ; Wheaton, 1985 ; Perrewé and Zellars, 1999 ; Schneiderman et al., 2005 ; Bedewy and Gabriel, 2015 ; Schönfeld et al., 2016 ; Reddy et al., 2018 ; Freire et al., 2020 ; Karyotaki et al., 2020 ). Third, the SWEMSBS used in our study and the data only measured positive mental health. Mental health pathways are numerous and complex, and are composed of distinct and interdependent negative and positive indicators that should be considered together ( Margraf et al., 2020 ). Fourth, due to the small effect sizes and unequal representation for different combinations of variables, our analysis for both the PAS and SWEMSBS included only summed-up scales and did not examine group differences in response to the type of academic stressors or individual mental health questions.

An additional limitation is that the participants in our study were a convenience sample. The testing service we used, prolific.co, self-reports a sample bias toward young women of high levels of education (i.e., WEIRD bias) ( Team Prolific, 2018 ). The skew toward this population was observed in our data, as 80% of our participants were women. While we controlled for these factors, the possibility remains that the conclusions we draw for certain groups, such as nonbinary students, ethnic/racial minorities, and men, may not be as statistically powerful as they should be. Moreover, our pre-screening was designed to recruit undergraduate level, English-speaking, 18–30-year-olds who resided in the United States. This resulted in our participant demographics being skewed toward the WEIRD bias that was already inherent in the testing service we used. Future research will aim to be more inclusive of diverse races/ethnicities, sexual orientations, languages, educational backgrounds, socioeconomic backgrounds, and first-generation college students.

Another limitation of our study is the nature of satisficing. Satisficing is a response strategy in which a participant answers a question to satisfy its condition with little regard to the quality or accuracy of the answer ( Roberts et al., 2019 ). Anonymous participants are more likely to satisfice than respondents who answer the question face-to-face ( Krosnick et al., 2002 ). We sought to mitigate satisficing by offering financial incentives to increase response rates and decrease straight-lining, item skipping, total missing items, and non-completion ( Cole et al., 2015 ). Concerns of poor data quality due to surveys offering financial incentives found little evidence to support that claim and may do the opposite ( Cole et al., 2015 ). On the other hand, social desirability bias may have influenced the participant's self-reported responses, although our anonymous survey design aimed to reduce this bias ( Joinson, 1999 ; Kecojevic et al., 2020 ).

Future Studies

Future studies should replicate our study to validate our results, conduct longitudinal cohort studies to examine well-being and perceived academic stress over time, and aim for a more representative student sample that includes various groups, including diverse races/ethnicities, sexual orientations, socioeconomic backgrounds, languages, educational levels, and first-generation college students. Additionally, these studies should consider examining other non-academic stressors and students' coping mechanisms, both of which contribute to mental health and well-being ( Lazarus and Folkman, 1984 ; Freire et al., 2020 ). Further explorations of negative and other positive indicators of mental health may offer a broader perspective ( Margraf et al., 2020 ). Moreover, future research should consider extending our work by exploring group differences in relation to each factor in the PAS (i.e., academic expectations, workload and examinations, and self-perception of students) and SWEMBS to determine which aspects of academic stress and mental health were most affected and allow for the devising of targeted stress-reduction approaches. Ultimately, we hope our research spurs readers into advocating for greater academic support and access to group-specific mental health resources to reduce the stress levels of college students and improve their mental well-being.

Utilizing two well-established scales, our research found a statistically significant correlation between the perceived academic stress of university students and their mental well-being (i.e., the higher the stress, the worse the well-being). This relationship was most apparent among gender and grade levels. More specifically, non-binary and second-year students experienced greater academic burden and lower psychological well-being. Moreover, women, non-binary students, and upper-level students were disproportionately impacted by stress related to the COVID-19 pandemic.

Studies regarding broad concepts of stress and well-being using a questionnaire are limited, but our study adds value to the understanding of academic stress as a contributor to the overall well-being of college students during this specific point in time (i.e., the COVID-19 pandemic). Competition both for admission to college ( Bound et al., 2009 ) and during college ( Posselt and Lipson, 2016 ) has increased over time. Further, selective American colleges and universities draw applicants from a global pool. As such, it is important to document the dynamics of academic stress with renewed focus. We hope that our study sparks interest in both exploring and funding in-depth and well-designed psychological studies related to stress in colleges in the future.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Institutional Review Board at Rutgers University. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

GB and MB contributed to conceptualization, study design, IRB application, manuscript drafting, and revision. XZ participated in the conceptualization and design of the questionnaires. HB participated in subject recruitment and questionnaire collection. KP contributed to data analysis, table and figure preparation, manuscript drafting, and revision. XM contributed to conceptualization, study design, IRB application, supervision of the project, manuscript drafting, and revision. All authors contributed to the article and approved the submitted version.

This study was made possible by a generous donation from the Knights of Columbus East Hanover Chapter in New Jersey.

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.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors wish to thank Shivani Mehta and Varsha Garla for their assistance with the study. We also thank all the participants for their efforts in the completion of the study.

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Keywords: academic stress, well-being, college students, Perception of Academic Stress, Short Warwick-Edinburgh Mental Well-Being Scale, COVID-19

Citation: Barbayannis G, Bandari M, Zheng X, Baquerizo H, Pecor KW and Ming X (2022) Academic Stress and Mental Well-Being in College Students: Correlations, Affected Groups, and COVID-19. Front. Psychol. 13:886344. doi: 10.3389/fpsyg.2022.886344

Received: 28 February 2022; Accepted: 20 April 2022; Published: 23 May 2022.

Reviewed by:

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*Correspondence: Keith W. Pecor, pecor@tcnj.edu

† These authors have contributed equally to this work and share first authorship

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• Published: 13 May 2024

The effects of social support and parental autonomy support on the mental well-being of university students: the mediating role of a parent–child relationship

• Arif Jameel 1 ,
• Zhiqiang Ma 1 ,
• Mingxing Li 1 , 2 ,
• Abid Hussain 1 ,
• Muhammad Asif 1 &
• Yan Wang 1  

Humanities and Social Sciences Communications volume  11 , Article number:  622 ( 2024 ) Cite this article

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The COVID-19 pandemic, as well as the complex response techniques employed to halt its spread, are both detrimental to mental and emotional well-being. Students’ lives have been damaged by social alienation and self-isolation. These effects must be detected, analyzed, and dealt with to make sure the well-being of individuals, specifically students. This research examines the impact of parent–child relationships, parental autonomy support, and social support on enhancing students’ mental well-being using data collected from post-COVID-19. The Potential participants were students from several universities in Pakistan. For this reason, we chose Pakistan’s Punjab province, with 8 prominent institutions, as the primary focus for data collection. A questionnaire was created to gather information from 355 students. For descriptive statistics, SPSS was used, while AMOS structural equation modeling was used to test hypotheses. The findings revealed that social support on mental well-being (standardized β  = 0.43, t  = 7.57, p  < 0.01) and parental autonomy support was significant and positively related to mental well-being (standardized β  = 0.31, t  = 5.016, p  < 0.01), and predicted parent–child relationships. Furthermore, the parent–child relationship strongly mediated the association between social support, parental autonomy support, and students’ mental well-being. This research proposes that good social support and parental autonomy support improve parent–children relationships and contribute to students’ mental well-being.

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Introduction.

COVID-19 has grown increasingly concerned with mental health and well-being in the past few years. Many research investigations have found that students have higher levels of psychological suffering than the overall people. This psychological tension of this virus among students has had significant and longer-term mental health repercussions, leading to low physical well-being results, including an increase in cardiovascular illnesses and lowly mental health (MH) results. Students suffer from the mental load of this impact more than grownups because they lack the grownup’s ways to cope and physical growth (Rawat and Sehrawat, 2021 ). Students who have a history of MH difficulties are more likely to suffer MH problems amid a crisis (Gavin et al., 2020 ). On the advice of the Emergency Committee, the head of the “World Health Organization (WHO)” stated the novel Coronavirus, also identified as “COVID-19”, is a Health Emergency of Worldwide Distress. COVID-19 has catastrophic impacts on the global business environment, schooling, and humanity (Priya et al., 2021 ). Health professionals designed a complex response plan to stem the spread of COVID-19 from the pandemic’s start. Being isolated or home quarantined was an essential part of the approach. One of the measures authorities have attempted to sluggish the spread of the virus is isolation from society. Isolation from society can affect mental health, increasing symptoms of anxiety, depression, and stress (Robb et al., 2020 ). There has been a surge in the number of students reporting mental health difficulties in the present years at colleges and universities. The underlying explanation might be university students’ inexperience, as they often struggle to handle stress, mainly when confronted with educational, social, and career-related challenges. Following research, the COVID-19 pandemic increases students’ chances of developing unhappiness and suicidality (Xiong et al., 2021 ). Following the closure of university campuses, students tended to see their educational future as bleak. Isolation from society and a lack of adequate and effective MH assistance exacerbated students’ fragile status. Because of these factors, young individuals pursuing university are now at a higher risk of acquiring MH disorders (Su et al., 2021 ).

Social support is instinctively understood, but ideas about definitions conflict when specific questions are raised. Family practitioners believe that social support is one of the possible keys to an individual’s well-being, especially for those going through significant life transitions or crises (Kaplan et al., 1977 ). The definition of “social support” varies usually among those who have studied it. It has been discussed in a general way as support that is “provided by other people and arises within the context of interpersonal relationships” Cooke et al. ( 1988 ) and as “support accessible to an individual through social ties to other individuals, groups, and the larger community” (Lin et al., 1979 ). Parent–children relations are interpersonal interactions formed by the interaction of parents and Child in blood and genetically related families. Parent–children ties are the first social associations to which individuals are exposed. It influences many facets of personality development, social cognition, and mental well-being (Lu et al., 2020 ). A lower degree of social support, in particular, is connected to greater levels of depressive symptoms (Wang and Peck, 2013 ). Social support refers to the standard of emotional assistance provided by others.

Furthermore, research shows that social support levels are closely related to measures of reduced stress and psychological discomfort, as well as improved well-being (Wang and Peck, 2013 ). Nevertheless, most research on youths’ social support focuses on their families, with relatively little research on their peers’ social support (Oktavia et al., 2019 ). Based on the gaps in existing knowledge, this research intended to determine whether there is a link between parent–child relationships during social seclusion caused by the pandemic and MH. This study additionally explored how social support (SS), parental autonomy support (PAS), and parent–child relationships are related to students’ mental health and well-being. It also looks into the role of the parent–child relationship as a mediator. The outcomes of this investigation are expected to increase understanding of the topic. Despite several research in the field of MH, there is still a literature gap on the roles and linkages of parent–child relationships and their mediator behaviors. This research provides a model for simultaneously investigating the roles and intervening factors. The focus of this investigation is on the following research questions. How can social and parental autonomy support affect students’ MH following COVID-19? How does the parent–children relationship mediate this relationship? Following an exhaustive assessment of the pertinent literature (Akram et al., 2022 ; Li et al., 2022 ), it was discovered that numerous research has been conducted to evaluate parents–children relationships and MH-associated issues, but to the best of our knowledge no research has been performed in Pakistan yet to determine the answers to these study topics will be provided utilizing a single theoretical model. Based on the available literature, the present study initially posits that social and parental autonomy support favorably promotes the student’s mental well-being and positively connects with a parent–child relationship in Pakistan. The study also hypothesizes that the parent–child relationship impacts the mental well-being of the students and mediates the connection between social support, parental autonomy support, and mental well-being. The research could have subsequent contributions: To begin, the research provides a detailed and systematic investigation of the concepts of the parents–children relationship, social support, parental autonomy support, and mental well-being. Second, by integrating the parents–children relationship, the study enhances the comprehensive analytical model that investigates the association between SS, PAS, and mental well-being. The model of the research describes the theoretical viewpoint in an innovative manner. Furthermore, the work has both practical and theoretical ramifications.

Literature review

Social support and mental well-being of students.

Social support is instinctively understood, but ideas about definitions conflict when specific questions are raised. Family practitioners believe that social support is one of the possible keys to an individual’s well-being, especially for those going through significant life transitions or crises (Kaplan et al., 1977 ; Wilcox and Vernberg, 1985 ). The definition of “social support” varies usually among those who have studied it. It has been discussed in a general way as support that is “provided by other people and arises within the context of interpersonal relationships” Cooke et al. ( 1988 ) and as “support accessible to an individual through social ties to other individuals, groups, and the larger community” (Lin et al., 1979 ).

Multiple research investigations have revealed that several internal elements influence young students’ mental well-being, notably the temperament of the students Ypsilanti et al. ( 2020 ), parental style Rinaldi and Howe ( 2012 ), and peer interaction (Holmes et al., 2016 ). One of the most significant macrosocial elements impacting students’ mental health is social support, which relates to the subjective and objective support they get from their circle of friends and how they utilize it (Shen, 2009 ). “Family support, friend support, and other support” are common sources of social support (Dahlem et al., 1991 , p. 760). Through social bonding, social support may reduce psychological stress and maintain or enhance a person’s mental and physical well-being (Cohen and McKay, 2020 ; Tao et al., 2022 ). Prior studies have found that social support can make parents more positive, enhance their mental and physical wellness, and improve their parenting efficacy (Yan et al., 2023 ). Once parents believe they have access to support and networks of friends, their psychological well-being rises (Chatters et al., 2015 ). Parents with higher social support are more nurturing and consistent in their parenting and less likely to use harsh parenting behaviors across a range of child ages Byrnes and Miller ( 2012 ), and social support may assist parents in managing how they react emotionally to their kids (Marroquín, 2011 ). Social support may also give parents developmental knowledge and advice on proper parenting practices, allowing them to adapt to their expectations and enhance their parenting abilities (Ayala-Nunes et al., 2017 ). A lack of or insufficient social support, on the other hand, maybe an indicator of risk for parental psychological wellness, leading to incorrect parenting behaviors (Belsky and Jaffee, 2015 ; Hu et al., 2023 ). Parents with psychological problems have fewer beneficial relationships with their kids, experience more instances of not positive interactions and enmity, express less efficiently, and are less responsive to their children’s actions (Herwig et al., 2004 ). As a result, parents’ perceived social support influences parenting ideas and conduct, which can impact children’s mental well-being development. As a result, parents’ perceived social support may be favorably related to the mental well-being of their children.

H1: Social support positively related to the mental well-being of students

Parental autonomy support and mental well-being of students

Following the self-determination theory Ryan and Deci ( 2000 ), “autonomy” is the fundamental cognitive or emotional need that leads to optimum growth and functioning, for instance, higher levels of educational accomplishment and improved psychological well-being of students (Vasquez et al., 2016 ). Parental support has been proven in studies to increase autonomy in young people (Inguglia et al., 2015 ). Parental autonomy support (PAS) refers to parents promoting emerging adolescents’ growing desires for independence, like liberty of expression, pondering, and making decisions (Soenens et al., 2007 ). Numerous research concentrating on European societies have found that parental autonomy support is connected with positive psychosocial adjustment in individuals (Froiland, 2011 ; Soenens et al., 2007 ). An empirical study, for example, has shown that autonomy support in intimate associations is an important predictor of mental well-being (Arslan and Asıcı, 2022 ; Shamir and Shamir Balderman, 2023 ). Likewise, Kins et al. ( 2009 ) found that PAS is related to greater mental well-being in Belgian young adults. Surprisingly, cross-cultural research found that parental autonomy support is connected to mental well-being in “Chinese and North American” teenagers Lekes et al. ( 2010 ), indicating that PAS benefits people working in a group environment.

Furthermore, according to the latest meta-analysis, the parental autonomy support association is greater when it reflects both parents instead of just moms and dads (Vasquez et al., 2016 ). Accepting this viewpoint, the present research emphasizes PAS. Whereas various research in Western cultures indicates the relationship between PAS and mental well-being, nothing is known about the advantages of “parental autonomy support” in a communal community or the fundamental connection between PAS and mental well-being. Through self-regulatory processes, culture can influence mental well-being, impacting how individuals think, feel, and conduct themselves in pursuit of mental well-being (Siu, Spector, Cooper, and Lu, 2005 ). Thus, we posit that PAS impacts the mental well-being of university students.

H2: Parental autonomy support positively related to the mental well-being of the students

Mediating effect of parent–children relationship

Parent–children connections are interpersonal interactions formed by the interaction of parents and Child in blood and genetically related families. Parent–children ties are the first social associations to which individuals are exposed. It influences many facets of personality development, social cognition, and mental well-being (Lu et al., 2020 ). Greater social interaction has been shown to improve parent–children interactions, increase parent–children warmth, and decrease parent–children animosity (Lippold et al., 2018 ). This might be attributed to two factors. On the one hand, social assistance may significantly enhance children’s quality of family life (Balcells-Balcells et al., 2019 ; Feng et al., 2022 ). Parents might have more time to dedicate to parenting, resulting in improved parent–children interactions. On the other hand, social support has been shown to lower parental stress, promote mental well-being, and favorably affect how parents act (Avila et al., 2015 ; Östberg and Hagekull, 2000 ). Social support can help parents get good parenting counsel and assistance (Dominguez and Watkins, 2003 ). Social support may assist parents in managing their feelings about their children, which leads to improved parenting practices and more parental warmth (Byrnes and Miller, 2012 ). Parent–children relationships and children’s mental well-being are inextricably linked. Parent–children connections are crucial in the development of children. Parent–children connections have a greater influence on the Child than other interpersonal interactions in the family and have a significant impact on the growth of a person’s personality, mental well-being, and adjustment (Nock et al., 2009 ).

Parent–children attachment and intimacy are significant manifestations of parent–children interactions. In the long run, the continuing emotional link between a kid and a caregiver is known as parent–children bonding. A strong bond is a vital basis for children’s healthy development and integration into society, and parent–children bonds remain stable as adolescents age (Juffer et al., 2012 ). It has been demonstrated that young people with solid parent–children bonds acquire more beneficial social abilities, have greater cognitive functioning, and have greater mental and physical wellness (Ranson and Urichuk, 2008 ). Parent–children attachment is the tight, warm relationship between parents and kids, which may be shown in positive interaction behaviors and close sentiments about one another (Chen et al., 2015 ). According to several types of research, the parent–children connection is the foundation of proper child development and the most consistent safeguard for healthy personal growth (Barber et al., 2005 ). Li et al. ( 2022 ) used the parent–child relationship as a mediator in their study to explore the impact of parental mediation on internet addiction. In a nutshell, students who have close, warm parent–children connections experience less externalizing and internalizing difficulties Lamborn and Felbab ( 2003 ), have a lower incidence of suicide ideation Harris and Molock ( 2000 ), and have improved psychological well-being. Thus, parent–children relationships may act as a mediating variable between SS, PAS, and the mental well-being of students.

H3: Parents–children relationship mediates the association between social support and the mental well-being of the students

H4: Parents–children relationship mediates the association between parental autonomy support and the mental well-being of the students

Figure 1 depicts the hypothesized study model. The direct impacts of SS and PAS on the mental well-being of university students were investigated first, followed by studying the other linkages and indirect effects among social support, parental autonomy support, parent–children relationships, and the mental well-being of the students.

Hypothesized model.

Materials and methods

Sampling technique and data collection.

The Potential participants were students from several universities in Pakistan. For this reason, we chose Pakistan’s Punjab province, with 8 prominent institutions as the primary focus. Due to Covid 19, it was projected that the majority of the students would remain at home and endure some form of psychological disorder with their families. To gather data on the research variables, we employed a validated questionnaire that was distributed to assistance desks/information desks of the selected institutions for self-rated replies. The datagathering period was from April to May (2023). Data were collected on-site. We used snowball sampling since the datagathering was connected to extremely subtle and individual concerns, such as mental well-being, social support, and parent–child relationships.

Furthermore, we requested assistance from the directorates of student affairs at the respective institutions in determining the target participants. We accompanied the recommendations offered by different scholars, such as those who recommended: “every item must be represented employing five samples,” that “samples of three hundred shall be regarded as appropriate,” who suggested that “the size of it ought to be twenty times bigger than the expected factors,” and who suggested that “ N  = 100–150” is adequate for conducting SEM (Anderson and Gerbing, 1988 ). Based on these scholars’ suggestions and the usual response percentage, we selected a sample size of 467 out of 355 that were found legitimate (response percentage of 76%). Male respondents comprised 55 percent, whereas female participants comprised 45 percent. Obtained surveys were utilized for research.

Measurement development

Each scale utilized in this investigation was taken and slightly modified from prior studies and had previously been authenticated by the researchers. Teti and Gelfand ( 1991 ) established the “Parent–Child Relationship” Scale, which is commonly used to measure the closeness of adolescents to their parents (Chen et al., 2015 ). It is made up of ten questions that relate to teenagers’ sentiments about their parents. Adolescents in this study were given questions like, “How openly do you talk with your parents?” The questions about perceived friend support were modified, and the sample construct was “I can count on my friends when things go wrong.”

Similarly, the study’s scale constructs of other people’s support were changed, and its example construct was “There is a special person in my life who cares about my feelings.” In this research, we assessed parental autonomy support developed by Soenens et al. ( 2007 ). It has five items: “My parents let me plan for things I want to do.” Furthermore, the assessment questions of mental well-being are measured by the five‐item scale of the World Health Organization. This scale was adapted from the study of (De Wit et al., 2007 ). Its three aspects, namely cognitive, emotional, and psychological health, were altered, and its construct was “I’ve been feeling optimistic about the future.” All of the constructs were measured on a “five-point Likert scale.” The Alpha for social support was 0.93. The Alpha value for parental autonomy support was 0.92. The Alpha for parent–child relationship was 0.90, and for mental well-being was 0.90.

Common method bias (CMB)

Since the data is collected all at once from a single source, bias concerns might surface and cast doubt on the study’s validity. The Harman single-factor test investigated the bias problem (Harman and Harman, 1976 ). The results demonstrated that each element of the suggested model could be separated into four variables, the first of which only explained 38.78% of the variation. According to this statistical value, normal biases must be lower than 50%. Therefore, our statistical data are free from prejudice.

Data analysis

We used Analysis of moment structures 25.0 to asses study hypotheses utilizing structural equation modeling (Shaffer et al., 2016 ). We used the two-step SEM technique Anderson and Gerbing ( 1988 ) recommended, beginning with CFA, to guarantee model adequacy. After that, an ultimate theoretical model was evaluated to evaluate the connections among every variable. Several fit indicators, such as 2/df, the CFI, TLI, the standardized root mean square residual (SRMR), and the root mean square error of approximation (RMSEA), were employed in the confirmatory factor analysis.

Descriptive statistics

The values for the mean, standard deviation, AVE, and Pearson’s correlations for each observed variable are displayed in Table 1 . The standard deviations ranged from 0.84 to 1.31, whereas the mean values were 1.43 to 2.94. Table 1 further reveals that the relationships between all variables analyzed are positive and substantial. Table 2 also indicates the DV of every factor for which the numerical values of average variance extracted are greater than the inter-correlational values, and the values of average variance extracted are also higher than 0.5 (Shaffer et al., 2016 ).

Measurement model

The measurement model in this work was evaluated using CFA Kline ( 2015 ), and Table 3 displays the standard factor loadings, Alpha, and CR of each component.

Social support, Parental autonomy support, Parent–child relationship, and mental well-being of students have Alpha of 0.92, 0.91, 0.90, and 0.88, respectively. These alphas exceed the suggested 0.70 threshold (Hair et al., 1998 ). The standardized factor loadings for Social support ranged from 0.78 to 0.86 for Parental autonomy support, 0.71 to 0.84 for the Parents–children relationship, 0.70 to 0.82, and 0.71 to 0.81 for the mental well-being of students. All factor loadings exceed 0.50 (Hair et al., 1998 ). The composite reliability (CR) ranges from 0.87 to 0.92 for Social support, Parental autonomy support, Parents–children relationship, and mental well-being of students, which is above the recommended value of 0.60 (Bagozzi et al., 1991 ).

In addition, we ran a serial-wise confirmatory factor analysis to ensure the model recognized different structures. The hypothesized 4-factor measurement model (Social support, Parental autonomy support, Parents–children relationship, and mental well-being of students) offered an appropriate fit to the data: χ 2  = 2693.55, Df = 946, χ 2 /df = 2.847, CFI = 0.92, TLI = 0.91, RMSEA = 0.05 and SRMR 0.04 (Table 3 ). The hypothesized 4-factor measurement model is the most suitable in each other models in Table 3 .

Table 3 shows that all observed items load the respective latent variables significantly. Other CFA models were contrasted with the proposed four-factor model. The validities are demonstrated by Table 4 ’s fit indices, providing a strong basis for evaluating the proposed four-factor model.

Hypotheses testing

We utilized a thorough structural equation modeling model with maximum likelihood estimation to analyze momentum structures and assess the study’s hypotheses. Simultaneously, hypotheses 1–2 (shown in Table 5 ) were supported by correlations (provided in Table 1 ) and SEM findings.

There is a strong positive correlation between students’ mental health and social support, as Hypothesis 1 suggests. Tables 1 and 5 provide the evidence we discovered supporting H1 (standardized β  = 0.43, t  = 7.57, p  < 0.01). According to the second hypothesis, there will be a beneficial correlation between students’ mental health and PAS. With standardized β  = 0.31, t  = 5.016, and p  < 0.01, H2 was supported.

H3 of our research uncovers that the ‘parents–children relationship significantly performs a mediating role in the association between social support and mental well-being of the students.’ Table 6 shows that when parent–child relationships are present, the β coefficient from social support and students’ mental health turns insignificant ( β  = 0.041; S.E. = 0.060; t  = 0.683; CI = −0.061, 1.012), but the indirect beta coefficient has a significant value ( β  = 0.149; S.E. = 0.063; t  = 2.365; CI = 0.337, 0.589). These findings demonstrate the mediating function of the parent–child bond in the association between students’ mental health and social support. The parent–child bond also acts as a mediator in the link between PAS and mental health, according to hypothesis 4. Table 6 shows a substantial mediating mechanism and a significant value for the beta coefficient. For H4, there is a substantial indirect correlation ( β  = 0.163; S.E. = 0.062; t  = 2.629; CI = 0.259, 0.352). Parental autonomy support and mental well-being have a direct link that eventually becomes negligible ( β  = 0.008; S.E. = 0.060; t  = 0.133; CI = −0.001, 0.013).

All formulated hypotheses of our study are accepted.

Mental health problems affect 10%–20% of students worldwide. Students’ susceptibility during the COVID-19 pandemic will likely influence this statistic. Poor mental health causes undesirable effects, including suicidal inclinations, behavioral disorders, and psychological abnormalities; hence, studies to remove or decrease the effects of bad mental health are critical. COVID-19 has made the already difficult state of youths and their mental health even more insecure. In the aftermath of a pandemic, the scale of COVID-19 the necessity for excellent research to fight MH concerns has grown exponentially. Keeping this information in mind, we developed our study subject and research questions and included parent–child relationships and linkages with students’ post-COVID-19 mental well-being of the pupils. The current research reviewed the literature on PAS, PSS, and people’s mental well-being after the COVID-19 pandemic. The literature study provided a vision of previous studies on the parent–child connection for mental well-being. According to research, the pandemic and its associated elements, such as quarantine, social isolation, and travel limitations, have been tense for students and other populations. Stress and worry caused by events such as closing schools, joblessness, poor healthcare, and uncertainty in education, job, and individual life have substantially influenced human mental and physical wellness (Pfefferbaum and North, 2020 ). We chose a paradigm that may serve the literature theoretically and practically, considering the significance of parent–child relationships after the pandemic. Prior study on the parent–child relationship has not investigated their role as a mediating variable in a unified model. The present research covers this gap in the literature by assuming that SS is positively linked with the mental well-being of students. The data analysis revealed that SS is substantially and highly positively associated with mental well-being; hence, H1 is accepted, in line with Cohen and McKay ( 2020 ), who discovered that social bonding and social support reduce psychological stress and enhance a person’s mental well-being.

Similarly, H2 investigated the association between parental autonomy support and mental well-being. It is also consistent with previous study findings that parental support has been proven to increase autonomy in young people, ultimately improving mental well-being (Inguglia et al., 2015 ). Because the conclusions indicated substantial values for each of these variables, H2 was also acceptable.

The mediation analysis was performed to determine if H3 and H4 were accepted or rejected. As previously stated in the findings section, mediating analysis was undertaken to check if the mediator increased the influence of independent variables on the dependent variable. Our research findings uncover that the ‘parents–children relationship significantly mediates the association between SS and mental well-being of the students.’ It can be viewed in the results section that the β coefficient from SS and mental well-being of the students turns insignificant in the attendance of the parent–children relationship, whereas the indirect beta coefficient has a significant value; this exhibits that the parent–children relationship plays a mediating role in the association between social support and the mental well-being of the students. Similarly, hypothesis 4 reveals that the parent–child relationship mediates the association between PAS and mental well-being. Table 6 shows that the beta value is significant, indicating a considerable mediation. The indirect association for hypothesis 4 is substantial, but the direct association between PAS and mental well-being turns insignificant. The H3 and H4 of our study are accepted.

Our findings reveal that all the proposed hypotheses were accepted, implying that SS has a good effect on the mental well-being of students and is related to a positive parent–child relationship. The findings then show that parent–child relationships positively influence mental well-being and play the role of mediating variable in the association between SS, PAS, and students’ mental well-being, which is in line with study findings that show that parent–child relationships mitigate the adverse influences of stress and foster mental wellness (Dam et al., 2023 ).

Implications

Our findings have far-reaching implications for medical practitioners, research organizations, and healthcare policymakers. Educational organizations should first become more aware of their students’ extra needs and mental health challenges. Future research should include people from various countries and ethnicities as COVID-19 control tactics and epidemic extent vary per country. Finally, the impacts of COVID-19 on students’ mental health have been overlooked. We urge instructors, higher education organizations, and mental health professionals to provide enough assistance to their students through the pandemic. Providing pupils with education to aid them in building self-efficacy, healthy parent–child relationships, and practical tools to cope with problems could help them handle the amplified stress that COVID-19 involves. It has been observed that durable and successful parent–child relationships were quite beneficial in helping pupils manage their stress. Administrators must appreciate MH practitioners’ function in supporting students seeking mental health support. Students’ capacity to tackle stress and create social support can assist them in escaping the harmful psychological impacts of the coronavirus outbreak. As a result, family, friends, and instructors should develop emotional resilience and enhance positive coping strategies among adolescents by adopting theory-tested treatments or programs. Because of constraints such as social isolation and lockdown, these treatments might be carried out in novel modes, for instance, webinars, online courses, and on-demand movies. Inter-professional probing programs and online mental behavior treatment boost students’ endurance and confidence (Schmutz, 2022 ). Furthermore, increasing social support could offer people a sense of higher psychological stability, reducing their fears and anxiety and helping them to function regularly during the pandemic. If students are urged to directly communicate their experiences and obstacles in their schooling after COVID-19, their morale will grow, and their MH will be preserved.

Limitations and future study

Our study has numerous limitations. For instance, this study relied on quantitative research; future research could use a qualitative or blended methodology to provide more intriguing outcomes. Secondly, the findings of this study were obtained by investigating eight educational institutions in Punjab province. Thirdly, because of the time limitation, we only carried out this study in one provincial unit. This research study might be broadened to other provincial units or nations in the future to generalize the study’s findings. Fourth, we obtained data from eight institutions; next, data from more institutions to be gathered to conduct the study. Finally, the current study included a mediating effect. Still, future studies may focus on using parent–child relationships as moderating variables. We suggest studying the reason for integrating PSS into the cognitive vulnerability model. As a result, new concerns have developed regarding the viability and significance of progressing to an integrative model, etiological paradigms, and innovative prospects for study and practical implementations.

The COVID-19 pandemic and the complex reaction techniques to halt its spread harm psychological and emotional well-being. Students’ lives have been damaged by social alienation and self-isolation. These effects must be detected, analyzed, and dealt with to guarantee the well-being of people like students. As a result, the present research sought to examine the influence of parental-child relationships, PAS, and SS in enhancing students’ mental well-being by gathering data from post-COVID-19. Students enrolling in Pakistani universities provided data. A survey for the survey was created to collect information from 355 students. SPSS was used to compute descriptive statistics, whereas AMOS structural equation modeling was employed to test hypotheses. These findings underlined the importance of the parent–child connection in dealing with complicated unfavorable conditions since it influences their mental results, particularly their psychological health. Optimistic and adverse relationships are opposed. Students who utilized primarily constructive relationship mechanisms with their parents experienced less emotional distress than those who employed more detrimental connection mechanisms with their parents (Budimir et al., 2021 ). Furthermore, the research emphasized the need for social support, such as friends and family, as well as parental autonomy support, in the fight against mental disorders. The findings also revealed that students require not only family support but also help from friends and others to create good relationships with parents to deal with psychological difficulties and stress produced by numerous sources.

Data availability

According to the confidential agreements with the participants, the dataset analyzed during the current study is not publicly available. The raw data supporting the conclusions of this article will be made available by the corresponding author upon reasonable request.

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This research is supported by the Jiangsu Funding Program for Excellent Postdoctoral Talent (No. 2022ZB643).

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Arif Jameel, Zhiqiang Ma, Mingxing Li, Abid Hussain, Muhammad Asif & Yan Wang

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AJ, MZ, YW, and ML conceptualized the study; MA and AH conducted the surveys and performed the Analysis; AJ and MZ wrote the first draft of the manuscript; all authors critically discussed the results, revised the manuscript, and have read and approved the manuscript.

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Jameel, A., Ma, Z., Li, M. et al. The effects of social support and parental autonomy support on the mental well-being of university students: the mediating role of a parent–child relationship. Humanit Soc Sci Commun 11 , 622 (2024). https://doi.org/10.1057/s41599-024-03088-0

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• Published: 28 May 2024

Associations between perceived occupational stressors and symptoms severity of depression, anxiety and stress among academic faculty: First cross-sectional study from Qatar

• Dalal Hammoudi Halat 1 ,
• Manar E. Abdel-Rahman 2 ,
• Ghadir Fakhri Al-Jayyousi 2 &
• Ahmed Malki 1  

BMC Psychology volume  12 , Article number:  302 ( 2024 ) Cite this article

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Mental health concerns among university faculty are on the rise, with reports of anxiety, depression, and occupational stress, impacting the higher education community. In Qatar, an assessment of faculty mental health has not been previously realized. The objectives of the current study were twofold: Firstly, to evaluate the extent of perceived occupational stress, depression, anxiety, and stress, and secondly, to assess the association among these mental health parameters.

A cross-sectional study was conducted among faculty using an online, self-administered, anonymous, voluntary survey. All faculty were included by sending the survey to their institutional emails. In addition to faculty demographics and general health status, the survey measured perceived stress due to academic job roles using the Faculty Stress Index (FSI) with its five distinct domains, and assessed faculty mental health using the Depression, Anxiety, and Stress Scale-21 items (DASS-21). Modified Poisson regression with robust variance was used to assess how FSI influences levels of depression, anxiety, and stress.

A total of 112 faculty responded to the survey. The highest faculty self-perceptions of mental health conditions were for anxiety (63% at least moderate), followed by depression (30% at least moderate), and least for stress (26% at least moderate). The overall mean FSI score was 48.8 ± 29.4; time constraint and rewards and recognition domains scored highest (18.5 ± 11.4 and 13.3 ± 9.3 respectively) while the departmental influence domain scored least (4.8 ± 4.4). Increased risk of at least moderate levels of self-perceived depression and stress were significantly associated with higher FSI score (p˂0.001). Increased risk of at least moderate levels of depression were less likely among faculty aged 50 years and above ( p  = 0.034), while increased risk of at least moderate levels of anxiety were more likely among faculty from humanities colleges ( p  = 0.027).

Conclusions

This is the first investigation of university faculty mental health in Qatar, indicating multifactorial perceived occupational stress, associated with higher perceived severity of mental health conditions. These baseline results establish links between specific occupational stressors for faculty and their mental well-being. As such, assessment of mental health conditions, controlling occupational stress, and developing tailored mental health interventions for faculty, are strategic to implement and foster well-being of academics. Further research into mental health of faculty and designing effective interventions that consider their specific stressors and associated factors are warranted.

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Introduction

The World Health Organization (WHO) defines mental health as a state of mental well-being that enables individuals to cope with life stressors, realize their own potential, learn and work efficiently, and contribute to their community and to the socio-economic development [ 1 ]. However, achieving such level of mental well-being remains a persistent struggle, with over one billion individuals globally suffering the toll of a mental or an addictive disorder [ 2 ]. Recent estimates of mental health burden point out to over 400 million disability-adjusted life years (DALYs) attributable to mental disorders, accounting for 16% of global DALYs, and an associated economic encumbrance that amounts to about 5 trillion USD [ 3 ].

Among mental disorders, depression and anxiety appear to be the most disabling conditions, and both are ranked among the top 25 leading causes of disease burden worldwide [ 4 ]. A debilitating disease characterized by depressed mood, reduced interests, and compromised cognitive function, depression affects about 6% of the adult population worldwide [ 5 ], and is the second leading contributor to chronic disease burden [ 6 ]. On the other hand, anxiety constitutes the largest plethora of mental disorders in most Western societies and persists as a leading cause of disability, with persistent fear, avoidance of perceived threats, and possibly panic attacks [ 7 ]. The global prevalence of anxiety is around 3.6% [ 8 ], and is higher in developing countries [ 9 ]. Apart from depression and anxiety, mental stress is a common and collective aspect of human existence. Evidence indicates that around two-thirds of the general population have encountered mental stress in the last two weeks, with nearly half of them describing it as “moderate or high” [ 10 ]. Stress has been pondered since ancient times, and it lingers as a product of the rapid, interconnected, and technologically advanced society of the 21st century [ 11 ], resulting in physical and mental health issues affecting individuals’ overall well-being [ 12 ]. In the workplace, occupational stress is a major issue precipitated by various job demands and experiences with short- and long-term implications [ 13 ]. Defined as “harmful physical and emotional responses that occur when requirements of the job do not match the capabilities, resources, and needs of the worker” [ 14 ], occupational stress affects a minimum of one-third of employees in various sectors, and is linked to several other disorders including insomnia, cardiovascular diseases, diabetes, and depression [ 15 ].

Across academia, mental health concerns have been documented at all levels, from undergraduate to graduate students, through junior and senior faculty [ 16 ], and calls for conversation over this issue have been on the rise [ 17 ]. For university faculty, the typically heavy workload, often tied to internal or external deadlines, competition for research resources, and uncertain job opportunities, can negatively affect mental well-being. Poor management practices, along with inadequate recognition and rewards, might further exacerbate faculty mental health status [ 18 ]. Several other factors contribute to escalate this so-called “invisible crisis” of mental health in academia [ 19 , 20 ]. Among these are challenges with maintaining work-life balance, navigating interactions with students, changes in higher education and research structures, and the recent, unparalleled transformations in academia due to the lasting impact of the COVID-19 pandemic, all of which seem to influence the mental well-being of faculty [ 21 ]. As such, the constant pressured to meet various demands of student interaction [ 22 ], teaching [ 23 ], promotion [ 24 ] and other tasks exerts its toll on faculty well-being, affecting their mental health [ 25 ].

Described as being prevalent but often widely ignored [ 18 ], mental health concerns among faculty usually occur in silence. Faculty tend to conceal their mental health problems from others due to fear of anticipated stigma, consequences on their careers, and confidentiality. The stereotype of high performance and prosperous achievements, usually nurtured in faculty during their early training and education, is greatly challenged during the career span. The demands of academia consistently confront individuals with shortcomings, promote perfectionism and competitiveness, and drive high expectations. This, in turn, perpetuates to faculty the belief that mental illnesses are inherent weaknesses, and that seeking help is a barrier to academic success, easily setting the stage for symptoms of anxiety, depression, and stress among this population [ 26 ]. In a survey of faculty in 2022 in the US, more than 80% of the respondents reported lifetime history of mental-health difficulties, and nearly half reported a diagnosed mental disorder [ 27 ]. More specifically, and in a recent investigation of faculty from 10 universities, 5.5% had increased symptoms of depression, 11.5% had increased symptoms of anxiety, and 23.4% had moderate to high stress levels [ 28 ], highlighting the need for investigations and remedial actions directed towards faculty well-being.

In terms of occupational stress, and despite the teaching profession previously viewed as a low stress occupation and faculty being resented for tenure, light workloads, and flexibility [ 29 ], faculty experience higher than normal levels of stress and ranked as second employment category in terms of worse-than-average psychological well-being scores [ 30 ]. The influence of job demands and the effects of the academic environment on mental health of faculty has been previously described [ 31 , 32 , 33 ], and the Faculty Stress Index (FSI), initially developed by Gmelch, Wilke, and Lovrich [ 34 ] is a reasonably explored tool in this regard. Through an investigation of the multidimensionality of faculty pressures and a consideration of uniqueness of the academic career, the FSI assembles a spectrum of roles that faculty undertake as teacher, researcher, adviser, university citizen, and departmental colleague. To capture these different responsibilities, the FSI measures five domains of perceived faculty stress: (i) reward and recognition domain, which pertains to rewarding external expectations for community and university services; (ii) time constraints domain, which confronts the number of tasks faculty members usually incorporate within their professional lives, including general duties such as paperwork, meetings, phone and visitor interruptions, and sufficient time for professional development, teaching, and services; (iii) departmental influence domain, which focuses on the extent to which faculty perceive their department as controlling over their work or the level of autonomy they have within their departmental facets, such as resolving differences, knowing evaluative criteria, and influencing decisions at departmental/ institutional levels; (iv) professional identity domain, which refers to reputation as a scholar and capability of setting and achieving professional goals, and is established on the basis of publications, presentations at professional meetings, and acquiring research grants; and (v) student interaction domain, which considers classroom instruction, course preparation, test administration, and advising [ 33 , 34 ]. Moreover, the FSI has been recently validated, and showed good internal consistency and reliability as an instrument useful to measure stress among faculty members [ 35 ].

Qatar University (QU) is the country’s largest national institution of higher education and continues to serve as Qatar’s primary university. Nowadays, QU has become a beacon of academic and research excellence in the Gulf region and internationally. The university is committed to providing high-quality education in areas of national priority, while aligning its programs with established international standards and best practices. QU hosts eleven colleges and offers a range of over 100 academic programs. In 2022, QU was recognized as a Healthy University by the World Health Organization (WHO). The concept of the health-promoting university is powerful, whereby it means integrating health into the culture, processes, environment, and policies of the institution. In addition, it means understanding and dealing with health within a framework that blends factors as choice and participation with goals for equity, sustainability, and health-conducive living, working and learning environments [ 36 ].

To our knowledge, a focused investigation of the mental health of faculty has not been realized previously at QU nor in Qatar, and a gap in literature exists in this regard at a national level. The objectives of the current study were twofold: Firstly, to evaluate the extent of perceived occupational stress, depression, anxiety, and stress among QU faculty, and secondly, to assess the association among these mental health parameters.

Study design

This study is part of a larger project aimed towards assessments of various aspects of mental health, well-being, and social determinants in a sample of faculty at QU. For this part of the project, a descriptive, cross-sectional, anonymous survey was conducted among QU faculty to assess their mental health aspects and investigate associated factors. The survey was electronic and self-administered, and faculty were asked to voluntarily complete it online. The conduct and reporting of this study follow the statement of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [ 37 ].

Participants

All QU faculty members, across all nine colleges, including faculty with different ranks (professors, associate professors, assistant professors, lecturers, teaching assistants, as well as part-time faculty) were invited to fill the survey. At the time of development of the study protocol, the number of faculty at QU was reported at 1355. QU staff who were non-academics, like support staff and administrators without a faculty contract were excluded. Also excluded were faculty who did not agree on providing their consent to participate. To collect responses, a non-probability based, convenience sampling method was used.

Data collection

Data were collected anonymously via an online survey sent to QU faculty members using their institutional email. The survey was prepared using Microsoft Forms application housed within the website of QU, and password-protected so that only the researchers can edit the questions and view responses. With support from the QU broadcasting team, a bilingual email announcement was sent to all faculty, whereby the study scope, research objectives and targeted participants were briefly described. The announcement was received only by QU faculty through their institutional emails. Participants were informed that their participation is voluntary and entails no risks nor benefits, and were ensured of anonymity and confidentiality of their responses. Completion of the survey till its end and submission of a response were considered as informed consent to participate. The email included a link and a QR code for participants to access each of the Arabic and English surveys.

Survey instrument and study variables

The survey instrument was developed by the authors in both Arabic and English to capture responses of all faculty, whereby some, particularly in the College of Law and the College of Sharia and Islamic Studies, were not English-speakers. Before the survey was launched, and for each of the Arabic and English versions, piloting was done with 5 faculty members who were invited to fill the survey and report any comments, feedback, or vague questions to the research team, for content validation of the survey questions. The piloting responses were not included in the analysis, but were used to implement amendments on the survey for content and clarity. The final survey instrument consisted of four sections. In the first section, faculty were asked about demographic data like age, gender, nationality and marital status, as well as data about their current academic position including their affiliated college, their highest academic degree, years of employment at QU, employment type (full-time or part-time), and whether they held any administrative tasks as academic administrators.

The second section of the survey was used to collect variables about general health and lifestyle habits of participants, including hours of sleep, physical activity, smoking (including cigarettes, vape and/or hookah). Also, participants were asked whether they have any medical diagnosis among hypertension, heart disease, diabetes, dyslipidemia, chronic kidney disease, chronic lung disease, or cancer. Another question inquired about participants’ diagnosis of a mental health disorder like anxiety, depression, schizophrenia, panic attacks, bipolar disorder, eating disorders, or others.

The third section of the survey included the FSI to evaluate perceived stress in academic settings considering different faculty roles, as previously described [ 31 , 34 , 35 ]. Briefly, 28 statements were used to measure the five domains of this instrument: reward and recognition domain was measured by 7 items, time constraint by 10, departmental influence by 3, professional identity by 3, and student interaction by 5. Participants were asked to rate the statements on an increasing score ranging from Not Applicable Pressure (0) to Very Slight Pressure (1), Slight Pressure (2), Moderate Pressure (3), Some Pressure (4), and Excessive Pressure (5). The higher the score on the sum of all the items, the higher would be the perceived faculty stress. Also, the higher the score on summation of statements for a particular domain, the higher would be faculty stress related to that domain. Bilingual members of the research team who were native Arabic speakers translated the English version of the FSI into Arabic.

The fourth section of the survey was intended to assess mental health of participants using the standardized Depression, Anxiety, Stress Scale-21 items (DASS-21) [ 38 ] and its Arabic translation [ 39 ]. This instrument included a set of three self-reported scales which provide independent measures of depression, stress, and anxiety, with recommended severity thresholds, and has been validated in the Arabic language according to previous studies [ 40 , 41 ]. Each of the three DASS-21 scales contains seven items, divided into subscales with similar content. The depression scale assesses dysphoria, hopelessness, the devaluation of life, self-deprecation, lack of interest/involvement, anhedonia, and inertia. The anxiety scale assesses autonomic arousal, skeletal muscle effects, situational anxiety, and the subjective experience of anxious affect. The stress scale is sensitive to levels of chronic nonspecific arousal. It assesses difficulty relaxing, nervous arousal, and being easily upset/agitated, irritable/over-reactive, and impatient. Each of the 21 items comprises a statement and four short-response options to reflect severity, scored from zero to 3. Participants were asked to read each statement and choose how much the statement applied to them over the past week, on a rating scale of: zero (Never - Did not apply to me at all); 1 (Sometimes - Applied to me to some degree or some of the time); 2 (Often - Applied to me to a considerable degree or a good part of time); or 3 (Always - Applied to me very much or most of the time). Scores for depression, anxiety and stress were calculated by summing up the scores for the relevant items (statements 3, 5, 10, 13, 16, 17, 21 for depression; statements 1, 6, 8, 11, 12, 14, 18 for stress; statements 2, 4, 7, 9, 15, 19, 20 for anxiety). As previously reported, the Cronbach’s alpha for the DASS-21 subscales were 0.886 for depression, 0.84 for anxiety, and 0.871 for stress, indicating good internal consistency [ 42 ]. The severity of the three DASS-21 scales were computed and expressed as normal, mild, moderate, severe, and extremely severe. An English version of the survey used in this study is included in Supplementary file 1 .

Statistical analysis

Data was analyzed using means and standard deviations for continuous variables and percentages for categorical variables. Bivariate analysis was conducted using Chi-square tests, Fisher exact tests, and independent two-sample t-tests. Modified Poisson regression with robust variance were used to examine both crude and adjusted association between levels of depression, anxiety, stress and FSI. Levels of depression, anxiety, and stress were further classified into two groups—Normal/Mild or Moderate (and above), as per standard cutoff points indicated in Table  2 . Crude and adjusted prevalence ratios with 95% confidence intervals were reported. Power calculations were performed at the conclusion of the study, considering the correlation coefficients among Depression, Anxiety, Stress, and FSI. Owing to the scarcity of similar studies, power was estimated assuming moderate correlation coefficients of 0.3 and 0.5. With these coefficients, an effect size of 0.15, a Type I error rate of 0.05, and a total sample size of 112, the calculated power of the study ranged from 45 to 66%. Stata version 18.0 was used for all analyses.

A total of 112 QU faculty participated in this research study (Table  1 ). The majority of the participants were Non-Qatari, PhD holders, full-time employees, males (54%), and aged less than 50 years old (64%). Nearly 45% of the participants had been at QU for more than five years, 43% were involved in administrative roles, and 55% were from non-health colleges.

In regards to their lifestyle and health status, 48.2% of the participants performed at least 150 min of physical activity per week, only half of them slept at least 7 h per night, and nearly a quarter have been diagnosed with a medical condition. About 13% of the participants reported being diagnosed with a mental health condition.

The descriptive analyses showed that the mean scores of depression, anxiety, and stress were 9.4 ± 9.8, 11.9 ± 7.8, and 12.9 ± 10.5, respectively (Table  2 ). About 30% of the participants reported at least ‘moderate’ (14+) depression symptoms severity, 63.4% reported at least ‘moderate’ (10+) anxiety symptoms severity, and almost 26% of the participants perceived their stress symptoms severity as at least ‘moderate’ (19+). The prevalence of anxiety, depression and stress among participants is shown in Fig.  1 .

Prevalence of depression, anxiety, and stress subscales

Participants’ perception of their stress was assessed by applying the FSI represented by its five domains: reward and recognition, time constraint, departmental influence, professional identity, and student interaction (Table  3 ). The FSI total score mean ( M ) was 48.8. Participants reported the highest stress score under the time constraint domain ( M  = 18.5), followed by reward and recognition domain (M  = 13.3), and student interaction ( M  = 6.2). Meanwhile, the least stress score was reported under the departmental influence domain ( M  = 4.8). The domains mean FSI scores are represented in Fig.  2 .

Mean FSI scores (error bars represent standard deviation)

The correlation between the FSI score and participants’ perception of their depression, anxiety, and stress symptoms severity were also examined (Table  4 ). The FSI score was significantly correlated with participants’ perception of their depression ( P  < 0.001) and stress (< 0.001) symptoms severity.

Results also showed that the mean score of FSI is 60.6 for those who reported at least moderate symptoms of depression (P = 0.005), and 67.0 for those who reported at least moderate symptoms of stress (P < 0.001) (Table  5 ). Under all the subscales of the faculty stress scores, participants were more likely to score high under at least ‘moderate’ (14+) symptoms of depression and at least ‘moderate’ (19+) symptoms of stress than normal/mild symptoms with the highest score related to the time constraint subscale; meanwhile, the lowest score is reported under the departmental influence.

In regards to participants’ demographics, female participants were more likely to score higher under at least ‘moderate’ symptoms of depression (60.6%) and stress (62.1%) compared to males (P = 0.052, 0.050, respectively) (Table  5 ). Non-Qatari participants scored higher under at least ‘moderate’ symptoms of anxiety (77.5%) and stress (72.4%), compared to Qatari participants ( P  = 0.005, 0.039, respectively),

;and participants from humanities (Law, Business, Sharia and Islamic Studies, and Education) (36.6%) were more likely to score higher under at least ‘moderate’ symptoms of anxiety compared to those from health-related colleges (11.3%) (P = < 0.001). Moreover, participants who had been for more than 5 years at the university scored higher under at least ‘moderate’ symptoms of anxiety (49.3%) and stress (65.5%) compared to those who spent less than 5 years at the university (P = 0.046, 0.027, respectively); meanwhile, full-time employees (96.2%) were more likely to report a higher score under at least ‘moderate’ symptoms of depression compared to part-time participants (P = 0.019). Finally, those who reported that they were not diagnosed with any mental health issue, were more likely to report at least ‘moderate’ symptoms of depression (72.7%)) and anxiety (81.7%) compared to those who reported being diagnosed with any mental health issue ( P  = 0.012, 0.044, respectively).

From the crude multivariable analyses (Table  6 ), the results showed that for every 10 points increase in FSI score, the prevalence of at least ‘moderate’ depression increased by 15% (P = 0.002) and of at least ‘moderate’ stress by 24% (P = < 0.001). All the subscales of the faculty stress scores were significantly associated with at least ‘moderate’ depression and stress symptoms severity. Under depression, it ranged between a Prevalence Ratio (PR) of 1.15 (95%CI 1.03, 1.29) for time constraint and 1.55 (95%CI 1.17, 2.06) for departmental influence related stressors. For example, for every 5-unit increase in departmental influence score, the prevalence of at least ‘moderate’ symptoms severity was higher by 55% compared to Normal/ Mild symptoms severity (P = 0.002). Under stress, it ranged between a PR of 1.29 (95%CI 1.11, 1.49) for reward and recognition and 1.67 (95%CI 1.24, 2.26) for departmental influence related stressors. For example, for every 5-unit increase in departmental influence score, the prevalence of at least ‘moderate’ symptoms severity was higher by 67% compared to Normal/ Mild symptoms severity ( P  = 0.001).

In regards to other sociodemographic characteristics, participants aged fifty years old and more were less likely by 57% (95%CI 0.20,0.94, P = 0.034) to report at least ‘moderate’ depression compared to younger participants, and male participants were less likely by 44% (95%CI 0.31,1.02, P = 0.058) and 47% (95%CI, 0.28,1.02, P = 0.057) to report at least ‘moderate’ depression, and at least ‘moderate’ stress, respectively, compared to females. On the other hand, Qatari faculty were 1.63 (95%CI 1.32,2.00, P = < 0.001) and 2.13 (95%CI 1.13,4.01, P = 0.019) times more likely to report at least ‘moderate’ anxiety and at least ‘moderate’ stress, respectively, compared to non-Qatari. Participants from humanities (Law, Business, Sharia and Islamic Studies, and Education), and “Arts and Sciences and Engineering” colleges were 1.87 (95%CI 1.22,2.87, P = 0.004) times and 1.63 (95%CI 1.04,2.55, P = 0.034) times more likely to report at least ‘moderate’ anxiety with compared to those from health-related colleges. Moreover, participants who had been working at the university between two-five years were 1.57 (95%CI 1.00,2.47, P = 0.049) times more likely to report at least ‘moderate’ anxiety symptoms than those who spent less time, and for those who had been working at the university for more than five years, in addition to report high under at least ‘moderate’ anxiety symptoms with PR 1.55 (95%CI 1.01,2.38, P = 0.046), they were 2.94 (95%CI 1.10,7.89, P = 0.032) times more likely to report at least ‘moderate’ stress symptoms compared to those who spent less that than five years at QU. In addition, part-time employees were 2.54 (95%CI 1.44,4.48, P = 0.001) times more likely to report at least ‘moderate’ depression symptoms compared to full-time employees. In regard to health and lifestyle factors, participants who used different tobacco products were 2.04 (95%CI 1.05,3.98, P = 0.036) times more likely to report high under at least ‘moderate’ depression symptoms compared to those who did not, and those who reported being diagnosed with mental health issues, were 2.43 (95%CI 1.41,4.17, P  = 0.001), 1.45 (95%CI, 1.12,1.88, P  = 0.005), and 2.06 (95%CI 1.07,3.97, P  = 0.031) times more likely to score high under depression, anxiety, and stress, respectively, compared to those who were not diagnosed.

From the adjusted multivariable analyses (Table  7 ), the results showed that for every 10 points increase in FSI score, the prevalence of at least ‘moderate’ depression increased by 13% (P = 0.013) and the prevalence of at least ‘moderate’ stress by 30% (P < 0. 001). Qatari faculty were 1.61 (95%CI 1.16,2.25, P = < 0.004) times more likely to report at least ‘moderate’ anxiety and 2,38 (95%CI 2.38 [1.11,5.10], P = 0.026) times more likely to report at least ‘moderate’ stress compared to non-Qatari faculty. Moreover, participants from humanities were 1.72 (95%CI 1.07,2.79, P = 0. 027) times more likely to report at least ‘moderate’ anxiety compared to those from -health-related colleges.

The results also showed that participants who had spent between two- five years at the university were 1.58 (95%CI 1.04,2.40, P = 0.030) times more likely to report at least ‘moderate’ anxiety symptoms compared to those who spent less time and part-time employees were 2.53 (95%CI 1.03,6.21,, P = 0.043) times more likely to report at least ‘moderate’ depressions symptoms compared to full-time employees. Related to participants’ health status, those who had been diagnosed with mental health issues were 2.05 (95%CI 1.12,3.74, P = 0.019), and 1.89 (95%CI 1.37,2.59, P = 0.001) times more likely to report at least ‘moderate’ depression and anxiety symptoms, respectively, compared to those who were not diagnosed.

This study is the first in Qatar to gain insights into mental health and associated occupational stressors among faculty. While promoting human values and scholarly excellence, the ecosystem of higher education institutions is not completely free from adversity, and can put academics at risk of different mental health–related concerns [ 43 ]. The cumulative effects of increasing workloads, long working hours, and challenges with work–life balance have been described as the roots of occupational stress in academia [ 44 ], underscoring the need for a focused investigation on these concerns in our institution. This becomes especially imperative in light of both, national contexts of prioritizing mental health [ 45 ], and calls for scrutinizing and improving mental health in academia [ 46 ].

Upon assessment of self-perceived faculty mental health using DASS-21, it was intriguing to find a minimum of 30%, 63%, and 26% of the participants having at least moderate levels of depression, anxiety, and stress respectively. These figures exceed reported results in a study from 10 big universities in the US, with ranges between 6 and 26% [ 28 ]. The reported prevalence of anxiety also exceeds that reported in a systematic review and meta-analysis of mental health of the general population during COVID-19, with an estimate of 38% [ 47 ]. Nevertheless, our numbers are still lower than those previously reported among academics in Australia [ 48 ] and the United Kingdom [ 49 ], with findings of almost 50% risk of psychological illnesses. As such, directed interventions aiming at improving the well-being of academic staff and contributing to more conversations on this topic are ultimately needed. For example, Recently, Lim and Colleagues [ 50 ] found that DASS-21 scores showed an improvement in faculty after mental health training interventions and professional support. Likewise, a Spanish study found that multiple mental and physical approaches improved self-perceived stress among academics [ 51 ]. For anxiety, mostly self-perceived among our participants, examples of digital and web-based interventions have proved previously effective in academic settings [ 52 ], and may be tempting to investigate for faculty.

Exploring occupational stressors of faculty using FSI showed a mean FSI score of 48.8, in stark difference with higher values reported using the same measure in other studies from that tackled public [ 53 ] and private institutions [ 35 ]. Further, multiple factors contribute to faculty stress, as shown by the FSI domains. The time constraint domain scored highest among all five domains in terms of perceptions of stress by faculty, followed by the rewards and recognition domain. Regarding time constraints domain, this construct is related to stress from time management of various commitments including heavy workload, committee services, meetings, out-of-office duties, and social obligations, among others. The issue of faculty time restraints and extra working hours has been previously documented in literature [ 54 , 55 ], with some reports of 40% of faculty working at least 10 extra hours per week [ 56 ]. As such, it is comprehensible that our participants perceived time demands as the main contributor to their workplace stress, in parallel to observations in academic institutions elsewhere. In modern universities with teaching, research and service missions, the production of knowledge through research, and the transmission of knowledge to students through teaching and to societal stakeholders through service, all have brought about an operational complexity, the sophistication of which is cascaded to faculty, pressuring them to accomplish more within a shorter time [ 57 ]. While faculty still consider themselves independent professionals, their traditional self-determination and autonomy regarding their working times have become subject to increasing scrutiny under the burden of calls for improved productivity, efficiency and accountability, thereby increasing occupational stress [ 58 ].

In terms of rewards and recognition, which was the second scoring domain in the FSI, participating faculty perceived evaluation criteria, community service recognition, teaching recognition, and other factors as drivers behind their stress. Previously, reward and recognition were shown to have significant correlation with different dimensions of work motivation and satisfaction in employees of various types of organizations not limited to academia, as reported by Danish and Colleagues [ 59 ]. Furthermore, rewards and recognition are regarded as top priorities for faculty motivation and/or satisfaction [ 60 ], as well as higher job performance [ 61 ]. More specifically, an investigation in an Australian university revealed that reward and recognition were perceived as actual barriers to promotion of faculty who did not conform to a ‘traditional’ structure of research expectations, whereby disadvantaged faculty from practice or professional backgrounds, or those who had heavy administrative roles, are not properly rewarded [ 62 ]. As such, our results conform with the body of evidence reporting the pressure that rewards and recognition exert on of faculty [ 63 , 64 ]. This probably calls for motivation of faculty through proper recognition and appreciation, whereby flexible guidelines, discipline-specific performance expectations, and career development pathways are reconsidered. A holistic approach to rewarding a broad range of educational roles may be beneficial, and requires strong advocacy to create changes in academic rewards in the interest of better faculty motivation and well-being.

On another end, the perceived stress by faculty due to each of student interaction and professional identity domains received almost one third of the scores for time constraint and about half of the scores for reward and recognition domain. Furthermore, the departmental interaction domain perceived stress scored the least among the five FSI domains, and observations on these three domains deviate from other published data [ 35 ]. For instance, student interaction was perceived to cause highest stress levels among faculty in KSA, according to the findings of Iqbal and Colleagues [ 31 ]. An in-depth look into items of the student interaction domain reveals that it addresses faculty normal tasks with students, like student evaluation, preparing class presentations, being evaluated by students for performance, and advising students, even those who may be inadequately prepared. With the majority of our sample being full-time PhD holders above the age of 40, it is likely that most of them have a rich teaching record and an extensive experience with handling student-related matters, reducing the contribution of these matters to perceived occupational stress. For junior faculty, it has been reported that teaching tasks not only occupy much of their time allowance, but also requires reasonable efforts in dealing with interaction-based activities that are entirely different from analysis-based research, easily leading to mental overload [ 65 ]. This might not be the case for our population, mostly consisting of senior academics. However, our findings may highlight the call for investing in more interesting teaching activities, as this may nurture the pedagogical process, while also contributing to less stress among faculty. Likewise, professional identity, focused on research support and professional conferences attendance, ranked fourth on the domains of perceived stress, probably due to the university prioritizing research and encouraging faculty visibility by presenting their scholarly work externally. The least perception of occupational stress by faculty was in the departmental influence domain, a construct emphasizing on departmental evaluation and resolving department conflicts. The relatively favorable results in such domain may indicate that faculty have feelings of belonging, and that one’s contribution to the department is recognized and valued, perhaps contributing to less stressful work days. Faculty members who perceive less stress in this area may be more collegial and generate amiability to improve the working atmosphere of their department and institution [ 33 ].

In multivariable analyses, increase in FSI score was associated with statistically significant likelihood of increase in severity of both depression and stress. Hence, while faculty juggle their various responsibilities, trying to sufficiently manage their time, get rewarded for achievements, and attend on various student needs, professional profiles, and departmental requirements, they may fall short of securing their own well-being, and can become at higher risk of encountering more severe mental conditions. Previously, Melnyk and Colleagues [ 28 , 66 ] reported that healthy lifestyle, sleep, and physical activity were associated with lower severity of mental conditions among faculty, namely depression and anxiety. Moreover, a study showed that faculty with initially high levels of occupational stress had significant improvements in this condition after targeted stress management interventions [ 67 ]. Similarly, mindfulness programs [ 68 ], de-stressors like yoga and art therapy [ 69 ], and behavioral coaching interventions [ 70 ] have been reported to positively affect mental health of faculty. However, despite importance, all these attempts remain deficient in addressing specific and tailored demands of faculty working environments, and creating custom-made interventions targeting faculty stress attributes, like those explored by the various FSI domains. According to a systematic review on mental health of academics, minimal research on managing mental health among faculty exists, and only limited information that measures the outcome of various mental well-being strategies is available [ 71 ]. As such, further research and robust study designs are needed in this area to concentrate on faculty-specific stressors and how they can be ameliorated in the workplace. Establishing routine mental health assessment, effective communication strategies, and continuous support are all imminent to improve the mental well-being of academics.

Noteworthy, multivariable analysis also showed a significance of less likely depression levels of at least moderate severity among faculty aged 50 years and above. Additionally, faculty who were nationals were statistically more likely to report at least moderate anxiety and stress levels. The latter two findings are in parallel with those formerly reported by Ganji and Colleagues [ 72 ]. While it could be hypothesized that with age, individuals experience a growth in maturity, enabling them to cultivate resilience by navigating through diverse stressors over the years, leading to improved emotional regulation and a reduction in symptoms of depression [ 73 , 74 ], the second finding of at least moderate anxiety and stress being more likely in Qatari faculty cannot be directly interpreted from our results, given that they constitute only 15% of the surveyed sample. Furthermore, faculty from humanities domains were more likely to report at least moderate anxiety compared to faculty from health-related colleges. In general, faculty and staff in medical schools may be inherently exposed to mental health issues among students, such issues being common among this population [ 75 , 76 ], triggered by demanding medical curricula and high financial costs [ 77 ]. Constantinou and Colleagues [ 78 ], in their review of medical faculty, point out that those faculty acknowledge the importance of mental illness, discuss symptoms with their students and provide support, and embrace the idea of being trained in this field. As such, we anticipate that health faculty, due to their background, might have better awareness about mental health issues, possibly making them personally less perceiving of some of them, like anxiety. Moreover, given the nature of their profession, health faculty may have a higher level of empathy and understanding of mental health [ 79 ]. They also often work under high-stress environments [ 80 ], and are part of a sector that recognizes mental health significance [ 81 ]. It is possible that all these integral constructs in health faculty roles could make them less likely to report self-perceived anxiety, and this may be interesting for an additional, focused investigation. Likewise, the observation of higher likelihood of at least moderate anxiety levels in faculty who have been at QU for 2–5 years compared to those who spent less time, may indicate a probable timeframe during which faculty may become deeply engulfed in their various academic duties and during which proper self-care and external support to avoid anxiety may be needed. Also, the higher likelihood of at least moderate depression among part-time faculty is a result that warrants additional study, especially with the latter finding recently reported among part-time workers [ 82 ]. The stress of having different jobs and the worrisome feelings about job instability, may instigate more mental health issues among this group of faculty.

The strength of this study lies in being pioneer in addressing faculty mental health from our institution, using validated tools, and in the use of a bilingual survey design that captures the prevailing cultural diversity of the studied population. Moreover, our study establishes links between specific occupational stressors for faculty and their DASS-21 scores, laying the ground for job-specific mental health investigations. However, our study does have limitations. First, we cannot neglect recall bias in a self-administered instrument; second, we expect some participants to have dropped out while answering the survey given the length of the instrument and the multiple statements in both the FSI and DASS-21, causing loss of some responses. Also, better conclusions from this study would be drawn out if the FSI Arabic version was back-translated, to ensure it captures more explicitly the insights of faculty who answered it in Arabic. While giving a preliminary outlook on how the mental health of faculty can be portrayed, and what essential strains in the work environment are significantly implicated, more remains to be captured in such and similar inquiries. This includes structured, periodic assessment of mental health and well-being of faculty, and exploring the efficacy of interventions that aim at reducing their specific occupational stressors. The preliminary findings from this study could be seen from the lens of proper practical recommendations that can support faculty mental health. The recognition and awareness regarding the need to improve faculty mental health can be the first step for implementing measures that favor their well-being. Organizational level measures, fair allocation of workload, and time management training could pave the way towards better mental health for academic faculty and foster a supportive environment for their wellness and ability to successfully thrive throughout the academic landscape.

In conclusion, perceived depression, anxiety, and stress in the academic setting is common among faculty, and mostly culminating from time constraints and faculty recognition to an extent higher than other factors like student interaction, professional identity and departmental issues. The higher participants perceived stress from their academic career, the more likely they were to experience more severe mental health symptoms, namely depression and stress. The implications of these findings indicate that controlling occupational stressors for faculty would be essential to avoid mental health conditions or at least, reduce their severity. Examining mental health conditions and their determinants for QU faculty members and purposeful consideration of the outcomes, will support the efforts of QU as a Healthy University, and will complement and guide its strategic efforts for a healthy campus. The results of this research will provide baseline evidence on the need for effective interventions towards occupational stress, orientation towards mental health, and informing policies on campus.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to the ethical concerns and participant anonymity, but are available from the corresponding authors upon reasonable requests.

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DHH and AM conceptualized this study idea. DHH did the literature review and obtained ethical approval. DHH, MEA and GFJ contributed to the design of the study. MEA ran all statistical tests and implemented the analysis. DHH and GFJ wrote the first draft of this manuscript. All authors have critically read the text and contributed with inputs and revisions, and all authors read and approved the final version of manuscript. AM supervised and administered the project.

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Hammoudi Halat, D., Abdel-Rahman, M.E., Al-Jayyousi, G.F. et al. Associations between perceived occupational stressors and symptoms severity of depression, anxiety and stress among academic faculty: First cross-sectional study from Qatar. BMC Psychol 12 , 302 (2024). https://doi.org/10.1186/s40359-024-01801-x

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Assessment of mental health and the participants, metrics of neighborhood nature, relationships between mental health and neighborhood nature, dose–response relationships between neighborhood vegetation cover and mental health, conclusions, supplemental material, acknowledgments, funding statement, references cited.

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Doses of Neighborhood Nature: The Benefits for Mental Health of Living with Nature

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Daniel T. C. Cox, Danielle F. Shanahan, Hannah L. Hudson, Kate E. Plummer, Gavin M. Siriwardena, Richard A. Fuller, Karen Anderson, Steven Hancock, Kevin J. Gaston, Doses of Neighborhood Nature: The Benefits for Mental Health of Living with Nature, BioScience , Volume 67, Issue 2, February 2017, Pages 147–155, https://doi.org/10.1093/biosci/biw173

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Experiences of nature provide many mental-health benefits, particularly for people living in urban areas. The natural characteristics of city residents’ neighborhoods are likely to be crucial determinants of the daily nature dose that they receive; however, which characteristics are important remains unclear. One possibility is that the greatest benefits are provided by characteristics that are most visible during the day and so most likely to be experienced by people. We demonstrate that of five neighborhood nature characteristics tested, vegetation cover and afternoon bird abundances were positively associated with a lower prevalence of depression, anxiety, and stress. Furthermore, dose–response modeling shows a threshold response at which the population prevalence of mental-health issues is significantly lower beyond minimum limits of neighborhood vegetation cover (depression more than 20% cover, anxiety more than 30% cover, stress more than 20% cover). Our findings demonstrate quantifiable associations of mental health with the characteristics of nearby nature that people actually experience.

The economic costs of anxiety and mood disorders , such as depression, have been estimated at €187.4 billion per year for Europe alone (Gustavsson et al. 2012 , Olesen et al. 2012 ). Alongside stress, they are some of the most prevalent work-related health issues (13.7% of all reported work-related cases; Eurostat 2012 ). This growing problem has, at least in part, been attributed to the increasing disconnect between people and the natural world that is resulting from more urbanized, sedentary lifestyles (the “extinction of experience”; Miller 2005 , Soga and Gaston 2015 ). This is supported by research that shows interactions with nature promote psychological restoration (Kaplan 1995 ), improved mood (Hartig et al. 2003 , Barton and Pretty 2010 , Roe and Aspinall 2011 ), improved attention (Hartig et al. 2003 , Ottosson and Grahn 2005 ) and reduced stress and anxiety (Ulrich et al. 1991 , Grahn and Stigsdotter 2003 , Hartig et al. 2003 , Maas et al. 2009 ).

The causal factors behind poor mental health are complex and diverse (Kinderman et al. 2015 ), and cultural and socioeconomic differences between regions may influence responses to interactions with nature (reviewed by Keniger et al. 2013 ). Understanding and capitalizing on the mechanisms by which natural environments provide psychological benefits nonetheless have the potential to be a novel and cost-effective approach to reducing the prevalence of some forms of mental ill health (Hartig et al. 2014 , Shanahan et al. 2015b ). Indeed, nature is likely to influence mental health through a range of mechanistic pathways (Shanahan et al. 2015b ). Attention-restoration theory proposes that the natural world promotes recovery from mental fatigue that occurs during the performance of cognitive tasks that require the prolonged maintenance of directed attention (Kaplan 1995 ), whereas stress-reduction theory argues that natural environments facilitate reductions in physiological arousal following stress (Ulrich et al. 1991 ). Both of these complementary theoretical frameworks lead to improved mental health from experiencing nature through decreased rumination, increased cognition, and reduced stress (Berman et al. 2012 , Jiang et al. 2014 , Tyrväinen et al. 2014 , Bratman et al. 2015 ).

Increasingly, evidence suggests that the availability and quality of neighborhood green spaces are associated with greater well-being (White et al. 2013 ) and lower levels of depression, anxiety, and stress (Beyer et al. 2014 ). These benefits may be gained from intentionally interacting with nature (e.g., through visiting neighborhood green spaces or spending time in a garden), from incidental interactions whereby people are exposed to nature as they engage in other activities (e.g., walking to the shops), or indirectly while not actually being present in nature (e.g., viewing it through a window; Keniger et al. 2013 ). The natural environment around the home is the nature that most people will experience every day and therefore, through all three kinds of nature interactions, will significantly contribute toward people's daily nature experience.

To date, most research into the health benefits of nature has considered the role of green spaces per se . The role of specific biological components of those spaces remains unclear, although these need to be identified effectively to guide planning to operationalize the use of nature as a health-promoting tool. In urban areas, two of the most visible elements of nature are vegetation cover and bird communities. The presence of vegetation has been found to have positive mental-health benefits, including but not limited to helping to reduce stress and promoting restoration from mental fatigue (e.g., Fuller et al. 2007 , Alvarsson et al. 2010 , Dallimer et al. 2012 ). Having more bird species in the environment and watching birds have been shown to be good for people's psychological well-being (Fuller et al. 2007 , Curtin 2009 , Brock et al. 2015 , Cox and Gaston 2016 ), whereas listening to bird song has been shown to contribute toward perceived attention restoration and stress recovery (Ratcliffe et al. 2013 ).

Previous studies investigating the relationship between components of biodiversity and psychological well-being have focused on measuring absolute diversity (how much diversity is estimated actually to be present; Fuller et al. 2007 , Luck et al. 2011 ) and/or the diversity that people perceive to be present (Dallimer et al. 2012 , Shwartz et al. 2014 , Belaire et al. 2015 ). However, these may not reflect the biodiversity that people actually experience. In particular, daily activity levels of people and other organisms often differ, so understanding the well-being effects of the diversity that people actually experience requires consideration of lower than actual values.

Here, we address two key questions. First, what components of nature are linked to positive mental-health outcomes? To answer this, we explore the relationships between three established self-reported measures of mental health for depression, anxiety, and stress and five metrics of neighborhood nature (vegetation cover, estimated actual abundance and richness of birds, and the abundance and richness of birds that people are likely to experience). Our second question is whether there is a threshold in the mental-health response. To answer this, we use dose–response modeling to estimate the point at which neighborhood vegetation cover (a tangible component of nature that relevant stakeholders can manage) influences the prevalence of depression, anxiety, and stress and the reduction in prevalence that could be achieved through enhanced exposure across the urban population.

We delivered an urban lifestyle questionnaire online (see Shanahan et al. 2016 for details) through a market research company (Shape the Future Ltd) to 1023 adults enrolled in their survey database. All the participants lived within the urban limits of the “Cranfield triangle,” a region in southern England, United Kingdom, comprising the three adjacent towns of Milton Keynes, Luton, and Bedford. Together, they constitute an urbanized area of approximately 157 square kilometers and an urban population of approximately 524,000 (according to the 2011 UK census). The triangle represents great variation in human population density (including examples of low- and high-density living), urban history, and urban form. The survey was delivered in May 2014, a period of reasonably mild weather when the respondents were most likely to engage with nature around their home, so the benefits of nature may be more pronounced. The participants were self-selecting and were compensated with either a nominal fee or a prize draw entry (see supplemental appendix S1 for ethical clearance). A subset of 263 respondents for whom there was both vegetation and bird survey data was then used in the analysis (see the metrics of neighborhood nature section below).

The survey respondents were asked to complete the short version of the Depression, Anxiety, and Stress Scale (DASS 21; Lovibond and Lovibond 1995 ). On a four-point scale, the respondents rated the extent to which each of 21 statements applied to them over the previous week (seven statements each for depression, anxiety, and stress; supplemental table S2a). To characterize the degree of severity for each mental disorder relative to the wider population, these scores were summed for each disorder before banding as normal, mild, moderate, severe, or extremely severe (table S2b; Lovibond and Lovibond 1995 ). If a respondent did not score a statement, then the relevant disorder for that respondent was discarded from the analysis (remaining respondents; depression = 248, anxiety = 259, stress = 240).

The survey collected sociodemographic and personal circumstance data that could potentially influence mental health, including age, gender, the primary language spoken at home, personal annual income, the number of days exercised for 30 minutes or more during the survey week (an an indicator of physical activity), self-assessment of health, and highest formal qualification. As a potential confound of recent nature exposure, we asked the respondents relatively how much time they spent out of doors in the previous week (supplemental table S1 shows how these variables were used for analysis). The respondents were requested to provide a full UK postcode so that their neighborhood could be characterized (one UK postcode covers approximately 20 households). On the basis of the postcode, the English Index of Multiple Deprivation (IMD) was used to assess the level of socioeconomic disadvantage ( sharegeo.ac.uk ; data sourced from data.gov.uk ). Finally, using the UK Gridded Population Based on the Census 2011 and Land Cover Map 2007 (Reis et al. 2016 ), we calculated neighborhood population density (see supplemental appendix S2 for full description of these two variables).

We measured five key components of nature that people were exposed to around the home. We first measured neighborhood vegetation cover as vegetation 0.7 meters (m) or more in height, within a 250 m buffer around the centroid of each respondent's postcode, approximately reflecting the viewscape from and the area immediately adjacent to people's homes. Vegetation cover maps were derived from airborne hyperspectral and light detection and ranging (LiDAR) data; full details of the spatial product development are provided in the supplemental appendix S3.

We conducted extensive bird surveys within the towns to generate a further four metrics of neighborhood nature. We estimated actual bird abundance and species richness as that recorded during early-morning surveys, when birds are most active and so most likely to be recorded (supplemental appendix S4). We also estimated the bird abundance and species richness that people were likely to experience as those birds that were recorded during afternoon surveys when most people are also active (appendix S4). These were derived from point count surveys, using distance sampling, at up to four locations within 116 tiles, each of 500 m × 500 m squares that were selected randomly across the three towns (full details are provided in appendix S4). We estimated neighborhood bird abundances and richness for those respondents whose 250-m neighborhood buffer overlapped with at least one bird survey location within a survey tile (respondents = 263; tiles = 84; see supplemental table S3 for sociodemographics of subset; supplemental figure S1 illustrates an example of overlap). This subset of respondents was used in subsequent analyses.

The neighborhood vegetation cover varied ninefold across the 263 respondents (supplemental table S4). Pearson's rank sum tests of the five metrics of neighborhood nature showed that actual and afternoon species richness were highly correlated (Pearson's r = 0.72, p < .0001), whereas the remaining nature variables were either weakly or not correlated ( r < 0.28; see supplemental table S5 for correlation matrix between nature variables).

We used ordinal regression to explore relationships between the five metrics of neighborhood nature and each mental-health disorder in turn. We incorporated age, gender, language, income, physical activity, self-assessment of health, level of education, relative time out of doors in the previous week, neighborhood population density and the IMD as covariates. We standardized the five nature metrics and neighborhood population density such that each had mean zero and standard deviation one. Because multicollinearity of more than 0.7 can severely distort model estimation (e.g., Dormann et al. 2013 , Cade 2015 ), we built two models for each mental state, including either actual or afternoon species richness in each along with the other three nature metrics and covariates. We used the Multi-Model Inference (MuMIn) package (Bartoń 2015 ) to produce all subsets of models on the basis of the global model and to rank them on the basis of the Akaike Information Criterion (AICc). Overdispersion in models is problematic in AICc analysis and may be due to not accounting for important covariates or multicollinearity, which can result in the selection of overly complex models that can lead to poor inference. Following Burnham and Anderson ( 2002 , p. 131) and Richards ( 2008 ), we reduced the retention of overly complex models by excluding from the set of candidate models all models that are more complicated versions of any model with a lower AICc value (i.e., nesting of models). To be 95% sure that the most parsimonious models were maintained within the best-supported model set, we then retained all models in which Δ AICc < 6 (Richards 2005 , 2008 ). We then calculated averaged parameter estimates and standard errors using model averaging among the retained models (Burnham and Anderson 2002 ).

People living in neighborhoods with higher levels of vegetation cover and afternoon bird abundances had reduced severity of depression, anxiety, and stress (table 1 ; figure 1 ). In contrast, there was no relationship with the estimated actual neighborhood bird abundance and species richness or afternoon species richness (table 1 ). The respondents who spent less time outdoors than usual in the last week had worse depression and anxiety (table 1 ). The respondents over the age of 45 years were less likely to suffer from depression than the younger respondents, whereas those between 46 and 60 years were less likely to suffer from anxiety (table 1 ). Mental health was positively correlated with self-reported physical health (table 1 ; inherent bias within self-reported surveys is here, at least in part, mitigated through large sample sizes and a robust ordinal regression analytical approach).

Nested model averaging of ordinal regression showing negative relationships between two visible components of nature around the home and three mental-health disorders while adjusting for sociodemographic factors.

Note: For the categorical variables (listed in italics), we show the model-averaged coefficients of variables relative to a comparative base factor level (e.g., age less than 30 years, so a positive coefficient suggests that those more than 30 years old have worse mental health. The other base factors are the following: gender, female ; language, English is the primary language spoken at home ; relative time outdoors, less time ; self-assessment of health, very poor ; education, 16+ years . The significant variables and factor levels relative to base level are shown as * p < .05; ** p < .01; *** p < .001.

For each mental-health disorder, we built two identical models, testing each measure of richness separately (see methods); the variable was not retained in the top nested models in which delta < 6.

The relationships between depression (a,b), anxiety (c,d), and stress (e,f), with 1) neighborhood vegetation cover (a,c,e) and 2) afternoon bird abundances (b,d,f). Error bars are standard errors and significant results are shown as: *P < 0.05; **P < 0.01.

Here, we have shown that metrics of nature that were most visible during the day and so most likely to be experienced by people, namely vegetation cover and afternoon bird abundances, were positively associated with a lower population prevalence of depression, anxiety, and stress. This may have arisen for a range of nonmutually exclusive reasons. First, experiences of visible nature may act to improve people's mental health, as was predicted from previous empirical studies of interactions between nature and well-being (see our introduction for references). Second, people with no or low mental-health disorders may be self-selected by electing to move into neighborhoods that are greener. Third, they may provide resources for birds, thereby increasing opportunities for closer interactions throughout the day. Therefore, it is unclear whether a lower population prevalence of poor mental health is shaped by the natural environment people live in or whether people move to a neighborhood that reflects that trait, or whether it is some combination of these factors. However, we found no relationship with the metrics estimating actual bird community composition or actual or afternoon species richness, nor were there relationships between mental health and covariates such as the IMD, education, or population density, although this is not entirely unsurprising given the complex nature of mental-health disorders and that previous studies have recorded wide variation in these relationships across different human populations (e.g., Das et al. 2007 ). The difference in the associations of actual and visable bird abundance with mental health is indicative of an effect of visible nature on mental health. Notwithstanding, future research needs to focus on further unpicking causal pathways, such as through studies of brain activity and function during exposure to nature (e.g., Bratman et al. 2012 , 2015 ).

The shape of the relationships between vegetation cover and the increasing severity of each mental-health disorder suggests that the greatest benefits were gained by those respondents with mild or moderate mental-health disorders (figure 1 ). This may be because the severity of depression often determines behaviors and therefore the degree to which people engage with nature. So people suffering from severe mental-health disorders may be less likely to venture outdoors, and the mechanisms behind their disorders may be different, thereby reducing the positive influence of nature. The respondents who spent relatively less time out doors in the survey week were more likely to report worse depression and anxiety. Intriguingly, this suggests that the relative nature experienced is a significant contribuing factor.

We found no relationship between mental health and either measure of bird richness or that of actual abundance. Given that most people cannot distinguish between species (Dallimer et al. 2012 , Shwartz et al. 2014 ) benefits may be provided through directly experiencing abundance, with richness contributing when people can see multiple species within a relatively small timeframe, such as around a feeder (Cox and Gaston 2015 ). Although the positive benefits for mental health of interacting with birds is compelling, in this study, it was not possible to determine the actual abundances of birds that the respondents interacted with; therefore, there may be more than one explanation for the positive associations between afternoon bird abundances and improved mental health. First, as seems likely, the abundances recorded by ecologists in the afternoon may be a good representation of the birds that most people experience and gain benefits from. Second, these abundances may be a proxy for another biological component.

We next calculated the dose–response of each mental-health disorder within the survey population that could be attributed to levels of neighborhood vegetation cover. We created a further three binary response variables: those with normal mental health for each of depression, anxiety, and stress and those suffering with mild or worse cases (Lovibond and Lovibond 1995 ). We used logistic regression for each binary response variable in turn to estimate the relative odds of occurrence in an individual given specific risk factors that were statistically significant in the previous analysis. Each covariate (i.e., risk factor) was transformed into a binary factor conveying high versus low risk (see supplemental table S6). For each mental-health disorder, we ran multiple logistic regression models. The first model contained the risk factors described above with the binary factor vegetation cover set at 10%, below which the risk of poor mental health was considered high. The model was then repeated applying an incrementally increased break point in vegetation cover (i.e., less than 15%, less than 20%, less than 25%, 30%, and less than 35%). We identified the point at which the health gains were first recorded as better than the null model on a plot of dose versus the odds ratio for use in the analysis described below (i.e., the confidence interval did not overlap with an odds ratio of one).

For each mental-health disorder, we calculated the population average attributable fraction to estimate the proportion of cases in the population attributable to each of the predictor variables (or risk factors; e.g., Rueckinger et al. 2009 ). Each risk factor was removed sequentially from the population by classifying every individual as low risk. The probability of each person experiencing mild or worse depression, anxiety, or stress was then calculated, in which the sum of all probabilities across the population was the adjusted number of disease cases expected if the risk factor were not present. The attributable fraction was calculated by subtracting this adjusted number of cases from the observed number of cases. The risk factors were removed in every possible order, and an average attributable fraction from all analyses was obtained (table 2 ).

Dose–response modeling shows the proportion of mental-health cases in the study population attributable to various risk factors (average population attributable fraction; AAF).

Note: We show a positive association between a reduced population prevalence of depression, anxiety, and stress and minimal thresholds of neighborhood vegetation cover* (depression more than 20% cover, anxiety more than 30% cover, stress more than 20% cover). An odds ratio above 1 indicates that the mental-health disorder is more likely to be present where the risk factor is present.

After accounting for covariates, the odds of having mild or worse depression were significantly lower when neighborhood vegetation cover reached a threshold of 20%, with gains in the odds ratio of 0.35 by 35% vegetation cover (figure 2a ). There was a significantly lower chance of having anxiety and stress after 30% and 20% vegetation cover respectively, although there was greater variability in the dose–response curve (figure 2b and 2c ). The power of the tests for all three mental-health disorders was reduced at higher levels of vegetation cover (indicated by wider 95% confidence intervals) because the proportion of the respondents reporting poor mental health declined at these levels; increasing the number of respondents may reduce the variability in the dose–response curves.

Dose-response relationships between neighborhood vegetation cover and the adjusted odds ratio from logistic regression, of a) depression, b) anxiety, c) stress (error bars are 95% confidence intervals). An odds ratio above one indicates an individual is more likely to have mental health disorders where the vegetation cover threshold is not met.

This threshold analysis has important implications for setting future research directions toward operationalizing nature as a tool for improving health and well-being for populations. Although there is unlikely to be a one-size-fits-all policy for optimizing nature in cities, establishing minimum levels of vegetation cover in neighborhoods is a practical approach that could be incorporated into city design.

The results suggest that if all the respondents lived in neighborhoods with vegetation cover of more than 20%, then the total number showing symptoms of depression would be reduced by up to 11%. The number of cases of anxiety and stress could be reduced by up to 25% and 17% if vegetation cover were more than 30% and more than 20%, respectively. Within the survey population, 38%, 76%, and 38% of the respondents were considered at risk of showing symptoms of depression, anxiety, and stress, respectively, because neighborhood vegetation cover levels were not met. In 2007, it was estimated that depression cost the English economy £7.5 billion, and anxiety cost £8.9 billion in health costs and lost workdays (McCrone et al. 2008 ). Although the causes of poor mental health are diverse, a simplistic calculation would be that if minimal levels of neighborhood vegetation cover were met, it has the potential to contribute toward an annual saving of up to £0.5 billion and £2.6 billion per year for depression and anxiety alone. Doubtless, the financial implications are marked. Consequently, manipulation of neighborhood vegetation and bird populations to “optimal” levels can and should be encouraged to be undertaken by both private and public stakeholders. There are multiple approaches available, including through the innovative addition of green infrastructure such as tree planting, through the addition of green walls and roofs (Tzoulas et al. 2007 ), or through the provision of supplementary food and nesting locations to increase local bird abundances (Fuller et al. 2008 ) and to bring birds into closer contact with people.

Research is starting to tease apart the mechanistic pathways behind how different components of nature benefit mental health (e.g., Bratman et al. 2015 , Shanahan et al. 2015a ). Although this study does not demonstrate causation per se , the positive relationships between two metrics of neighborhood nature and better mental health are consistent with a mechanistic effect. Indeed, the dose–response relationship for depression—and to a lesser extent, anxiety and stress—is considered to provide some evidence of causality according to Hill's criteria (Hill 1965 ). These benefits are likely to be provided via two pathways: first by increasing the attractiveness and appeal of green space such that people are more likely to spend time out of doors and therefore increase the likelihood that they will engage in physical or social activities, and second by increasing the visual complexity of the landscape, enhancing its effect on mental restoration and well-being (Shanahan et al. 2015b ). However, at the same time, it is important to acknowledge that living close to too much, or inappropriate, nature can also provide a range of dis-services, such as the destruction of property from vegetation and breeding birds (e.g., Rock 2005 ) or increased levels of vegetation leading to feelings of decreased safety in some neighborhoods (e.g., Kuo et al. 1998 ). Future research into “best” doses of nature would benefit from exploring the trade-offs between the benefits and dis-services.

Although the causes and drivers of poor mental health are diverse (Kinderman et al. 2015 ), this study suggests that even low levels of key components of neighborhood nature can be associated with better mental health, providing promise for preventative health approaches. This study shows that quantifiable reductions in the population prevalence of poor mental health can be achieved if minimal thresholds of vegetation cover are met. This has important implications for policy to set minimum levels of neighborhood nature and paves the way to test for health gains that arise from specific interventions. Obviously, optimized levels of nature are not a silver bullet for the prevention or treatment of mental-health problems, but it is an approach that can and should be applied in conjunction with existing frameworks such as medical and social services, reducing crime, and increasing community-driven action. Indeed, optimizing the key components of nearby nature has been shown to change behavior toward increased social cohesion (e.g., Weinstein et al. 2015 ) and green exercise (e.g., Mitchell and Popham 2008 ).

Supplementary data are available at BIOSCI online.

We thank M. Evans and M. Gregory for their fieldwork and Professor Harris and Dr. Evans for their support and helpful consultations. We would like to thank six anonymous reviewers for their helpful comments. The data are available on request from the corresponding author and will be made available from mid-2017 at the NERC Environmental Data Information Centre.

DTCC, HLH, KEP, GMS, KA, SH and KJG were funded by the Biodiversity and Ecosystem Service Sustainability project, Natural Environment Research Council grant no. NE/J015237/1. DFS is supported through Australian Research Council Discovery Grant no. DP120102857 and the Centre of Excellence for Environmental Decisions (CEED, Australia); RAF holds an Australian Research Council Future Fellowship.

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Maintaining health and well-being as we age

Although many older adults demonstrate high levels of resilience, they are also more prone to social isolation and loneliness than any other age group. Having strong social connections is especially important for mental health and well-being as we age, and is associated with lower instances of depression and anxiety.

Simon Fraser University (SFU) professor of mental health and aging Theodore D. Cosco researches a range of factors that promote healthy aging and resilience in older adults, from digital interventions to physical activity. He leads the Precision Mental Health Lab , a transdisciplinary research group dedicated to community-engaged and innovative technological approaches to improve well-being across all age groups.

One of his major research projects is studying data from the Canadian Longitudinal Study on Aging (CLSA). Cosco is a co-investigator on the CLSA, a national, long-term study of more than 50,000 Canadians who were 45 to 85 years old when the program began in 2009. Over 160 researchers from 26 universities across Canada are involved in the CLSA.

Cosco and colleagues, including three PhD students he supervises:  Lucy Kervin , Shawna Hopper , and Indira Riadi have found that during the coronavirus pandemic, the decreased ability to participate in social and physical activity was associated with increased risk of depression and anxiety in older adults.

These findings are outlined in Worsened ability to engage in social and physical activity during the COVID-19 pandemic and older adults’ mental health ,   published in Innovation in Aging .

We spoke to professor Cosco about his research.

What did your research reveal about older adults’ diminished ability to engage in physical and social activities during the coronavirus pandemic?

Our team used data from 24,108 participants surveyed during the first nine months of the COVID-19 pandemic and found roughly 22% screened positively for depression and 5% for anxiety.

Generally, older adults who reported worsened ability to participate in social and physical activities during the pandemic had poorer mental health outcomes than those whose ability remained the same or improved. We also found that participating in these activities had a buffering effect on depression and anxiety.

How does this research apply now that the pandemic is behind us? Do you have recommendations?

Our findings highlight the importance of fostering social and physical activity resources to mitigate the negative mental health impacts of future pandemics or other major life stressors that may affect the mental health of older adults. Beyond the pandemic these results highlight the importance of staying socially and physically active. You do not need to be socializing seven nights a week, nor do you need to be running marathons. Doing anything is better than nothing, so finding ways to integrate socializing and exercising into one’s life is an excellent strategy. Pick up the phone, walk to the shops, or find a way that you can integrate activity into one’s own life.

How do you approach the study of vast amounts of data from the CLSA? Do you have specific research questions to investigate, or does the study reveal topics that you want to pursue?

When working with large datasets, it is crucial to understand the types of data included, their collection dates and their sources. Once familiar with the available data, you can delve into current research and literature to formulate hypotheses. With extensive datasets, specificity in your initial hypotheses and deliberate in your analysis approaches are vital. Because of the dataset's size and the significant statistical power it provides, running numerous models to explore every possible outcome can often lead to “statistically significant” findings that occur by chance. This practice, known as “fishing” or “data dredging,” is discouraged because it may result in misleading associations. Therefore, it's important for us to be very purposeful in testing our hypotheses to avoid these issues.

In a previous Scholarly Impact of the Week, you discussed how during the pandemic older adults and their families quickly adopted the use of technology to increase connectedness. Is this trend still going strong, and do you have new insights on technology and older people?  

During the pandemic, older adults and their families rapidly embraced technology to stay connected, a trend that remains strong today. This period really spotlighted both the advantages and limitations of our current technology. It became clear that tech companies need to move away from a one-size-fits-all approach. Products specifically designed with older adults in mind—taking into account their unique needs and preferences tend to be more successful. These intentionally crafted tools are not only more widely accepted but also have a more significant impact. The pandemic has shown us the importance of such tailored technology solutions in enhancing social connectedness for older populations.

For more: See professor Cosco’s previous Scholarly Impact of the Week article, Understanding the impacts of COVID-19 on older adults , and visit the Precision Mental Health Lab web page.  

SFU's Scholarly Impact of the Week series does not reflect the opinions or viewpoints of the university, but those of the scholars. The timing of articles in the series is chosen weeks or months in advance, based on a published set of criteria. Any correspondence with university or world events at the time of publication is purely coincidental.

For more information, please see  SFU's Code of Faculty Ethics and Responsibilities  and the  statement on academic freedom .

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Emotional Intelligence and Psychological Well-Being in Adolescents

Joan guerra-bustamante.

1 Department of Psychology, Faculty of Teacher Training College, University of Extremadura, 10071 Cáceres, Spain; se.xenu@bgnaoj (J.G.-B.); se.xenu@noelb (B.L.-d.-B.); se.xenu@zepolmv (V.M.L.-R.)

Benito León-del-Barco

Rocío yuste-tosina.

2 Department of Educational Science, Faculty of Teacher Training College, University of Extremadura, 10071 Cáceres, Spain; se.xenu@etsuyoicor

Víctor M. López-Ramos

Santiago mendo-lázaro.

The present study aimed to analyze the association between of the dimensions of emotional intelligence (attention, clarity, and repair) and different levels of perceived happiness (low, medium, and high) in adolescents. The sample consists of 646 students in the first, second, third, and fourth years of Secondary Education, 47.5% females and 52.5% males, between 12 and 17 years of age. The instruments used were the Spanish version of the Trait Meta Mood Scale-24 Questionnaire to measure perceived emotional intelligence and the Oxford Happiness Questionnaire. Multinomial logistic regression analysis and receiver operating characteristic (ROC) curve analysis were performed. The results suggest that as the capacity of understanding and regulation of emotional intelligence increases, happiness also increases. Adolescence is seen as an ideal time in life to encourage the development of emotional capacities that contribute to the greater happiness of individuals. In this way, the present study stresses the need to carry out practices leading to improvements in the adolescents’ emotional intelligence and therefore increase their happiness and emotional well-being.

1. Introduction

The study of happiness and emotional well-being in young people has expanded exponentially in recent years. Psychology has traditionally focused on unhappiness and paid little attention to positive aspects of human potential [ 1 ]. This approach has been evident when studying adolescence, since this period of life implies many changes and it has been long described as a moment of stress and difficulties [ 2 ]. This conception of adolescence is currently fairly different for studies do not only describe the adolescent as a source of problems but also as a valuable asset in a development process [ 3 , 4 ]. This change took place with the arrival of positive psychology, as one of its objectives is to promote psychological research and practice in such areas as positive traits (strengths), positive emotions, and their contribution to well-being [ 5 ].

1.1. Happiness or Psychological Well-Being

As for the study of happiness, it is essential to point out that there is no consensus about how to define it. One of the most accepted theoretical approaches states that the construct happiness refers to an emotional and cognitive type of psychological state [ 6 ], a positive affective component in which positive emotions and the subjective interpretation of well-being are fundamental [ 6 , 7 , 8 , 9 , 10 , 11 , 12 ].

On a theoretical level, the debate on happiness has two main approaches: 1) the hedonic approach, that affirms that happiness is the presence of positive affection and the absence of negative affection; and 2) the eudaimonic approach, that states that happiness is the consequence of full psychological functioning by means of which the person develops his or her potential [ 13 ]. In line with eudaimonism, it is noteworthy to mention the psychological well-being multidimensional model [ 14 ], focused in the fulfillment of human potential through six key features: autonomy, environmental control, personal growth, positive relationships with others, purpose in life, and self-acceptance [ 15 ]. Both approaches can be integrated in the “three dimensions of happiness” model [ 1 ] which are: 1) a pleasant life, understood as a pleasant feeling towards past, present and future; 2) a committed life, by using positive individual features, including character strengths and talents; and 3) a meaningful life, which means to serve and to belong to positive institutions. Subsequently, this model favored the appearance of 24 Strengths Model [ 16 ] which focuses on studying happiness in strengths and virtues.

Accordingly, they reinforce the idea of the existence of factors that determine happiness [ 17 ]. Then we find the Science of Happiness [ 12 ] which claims that happiness can be increased by the individual himself by means of certain activities. For that matter, such a vital period as adolescence is the ideal moment to increase it. In recent years, different theoretical approaches have defended a positive comprehension of adolescence, a crucial stage characterized by plasticity, the acquisition of competences and the achievement of satisfactory levels of well-being and positive adjustments [ 17 ]. It is a time when the capacity to appreciate satisfaction with life and well-being increases in a critical and conscious way [ 18 ]. Specifically, teaching adolescents to be happy functions with three main goals: as an antidote against depression, as a means of increasing life satisfaction, and as a way to enhance learning and creative thought [ 19 ].

1.2. Emotional Intelligence

One of the variables that could help to this increase of happiness during adolescence can be emotional intelligence [ 20 ]. There are two relevant models of emotional intelligence: Mixed Models and Ability Model. Mixed Models state that emotional intelligence is a compendium of stable personality features, socio-emotional competences, motivational aspects, and different cognitive abilities [ 21 , 22 , 23 ]. On the other side we find the Ability Model [ 24 ] which considers emotional intelligence as an ability focused on emotional information processing [ 25 ]. Ever since Model of Emotional Intelligence, this construct is defined as a type of social intelligence that involves the ability to monitor one’s own and others’ emotions, to discriminate among them, and to use the information to guide one’s thinking and actions [ 24 ]. Subsequently, said authors included in their definition abilities related to cognitive and emotional clarity, perception, and repair that could generate feelings that eased thinking and abilities of cognitive and emotional regulation [ 26 ]. In order to measure this construct, they designed questionnaire TMMS-24, which assesses Perceived Emotional Intelligence through three factors: attention to emotions (capability to feel and express feelings properly), emotional clarity (capability to understand the own emotional states), and emotional repair (capability to correctly regulate emotional states).

1.3. Happiness or Psychological Well-Being and Emotional Intelligence

Scientific literature highlights the major role of emotional intelligence when determining individual happiness [ 20 ]. Numerous researchers have related emotional intelligence with psychological constructs that are closely associated with happiness, such as subjective well-being [ 27 , 28 ], higher rates of positive emotional states and decrease of negative emotional states [ 29 ], satisfaction with life [ 20 , 30 , 31 , 32 ], better psychological functioning and social competence [ 33 ], and better social relations; and negative associations with loneliness [ 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. Other studies have focused on the relationship between emotional intelligence and variables connected with well-being in young people, such as physical and mental health [ 41 , 42 , 43 ] and perception of stress [ 44 ]. There is therefore clear evidence that capacities of emotional intelligence predict aspects related to personal well-being and a positive relation between life satisfaction and subjective happiness [ 45 , 46 ].

For this matter, Hills and Argyle [ 47 ] composed the Oxford Happiness Questionnaire, which evaluates subjective happiness from these psychological dimensions, including items focused on life satisfaction, positive emotions, physical and mental health, or social relationships.

More specifically, studies made from mixed models note that the trait emotional intelligence is a constellation of capacities and self-perceived attitudes related with emotion [ 48 ]. In this regard, different studies note the existence of a positive correlation between emotional intelligence as a trait and perceived happiness [ 49 , 50 ]. On the other hand, from the ability model, research based on Spanish adolescent subjects shows that the abilities of clarity and repair are positively correlated with life satisfaction whereas attention correlates negatively in adolescents [ 51 ]. In the same way, the dimensions of emotional recognition and expression, and the control of emotions mediate in the relationship between fully dispositional mindfulness and subjective happiness [ 52 ]. However, it should be considered that self-perceptions and attitudes associated with people’s emotions—such as emotional regulation, relationship skills, and social competence—determine variation in happiness to a large degree [ 50 ]. Henceforth, research shows that emotional intelligence abilities imply a skill that allows adolescents to guide their thoughts and ponder over their emotions, helping them to improve their well-being levels [ 53 ]. These studies suggest that important interventions may be performed to promote flourishing and happiness, enhancing emotional intelligence through specific training [ 54 ].

The present study seeks to analyze in a sample of adolescents, the association between of the dimensions of emotional intelligence (attention, clarity, and repair) and different levels of perceived happiness (low, medium, and high). It will also identify the sensitivity and the ability to distinguish scores obtained in the Spanish version of the questionnaire Trait Meta Mood Scale [ 55 ], from which high happiness is more likely to exist.

2. Materials and Methods

2.1. participants.

The sample consists of 646 students in the first, second, third, and fourth years of Secondary Education, 47.5% females and 52.5% males, between 12 and 17 years of age. The sampling was carried out by selecting eight schools in the Community of Extremadura (Spain) at random.

2.2. Instruments

2.2.1. trait meta mood scale.

The Spanish version of the questionnaire Trait Meta Mood Scale (TMMS-24) [ 55 ] has been used to evaluate perceived emotional intelligence. The questionnaire is formed by 24 items with a Likert-type five-point answer scale (1 = Do not agree, 5 = Totally agree). Three dimensions are evaluated (eight items per dimension): attention (ability to feel and express feelings appropriately); clarity (understanding of emotional states); and repair (appropriate emotional regulation). Each dimension can be classified into three traits depending on the score: Attention; 1) Attention should be improved; 2) Adequate attention; 3) Excessive attention: Clarity; 1) Clarity should be improved; 2) Adequate clarity; 3) Excellent clarity: Repair; 1) Repair should be improved; 2) Adequate repair; 3) Excellent repair. The internal consistency measured with Cronbach’s alpha was 0.826 for attention, 0.825 for clarity, and 0.833 for repair.

2.2.2. Oxford Happiness Questionnaire

The Oxford Happiness Questionnaire (OHQ) [ 47 ]. The objective of this questionnaire is to measure happiness in general, i.e., psychological well-being. A series of statements about happiness are given and the participants indicate their degree of agreement with each one. In psychometric terms, it consists of 29 items or 29 potential sources of happiness and the participants consider the extent to which they form part of their experiences. It employs a six-point Likert-type scale (1 = I totally disagree, 6 = I totally agree). The lowest score that can be obtained is 1 (if Answer 1, ‘I totally disagree’ is chosen in all the statements) and the highest is 6 (if Answer 6; ‘I totally agree’ is chosen for all the statements). In this study, the internal consistency measured with Cronbach’s alpha was 0.800.

2.3. Procedure

The procedure followed for data collection was the administration of the questionnaires by classroom group. In the first place, the educational centers were contacted to explain the objectives of the study and request authorization for the completion of the questionnaires. We followed the ethical guidelines of the American Psychological Association regarding the informed consent of the parents, due to participants’ being underage. Likewise, anonymity in the answers, the confidentiality of the obtained data, and its exclusive use for research purposes was assured. The administration of the questionnaires was carried out during school hours; it took around 50 min. in an adequate climate and without distractions. This study was approved by the Bioethics and Biosafety Committee of the University of Extremadura (no. 0063/2018).

2.4. Statistical Analysis

Firstly, we submitted the data to the assumptions of independence, normality, homoscedasticity and linearity required by the classical linear model. We did not find normality or homoscedasticity in our data, so we decided to perform a multinomial logistic regression analysis. Although it may seem that transforming a variable initially classified as continuous to categorical would mean losing information, during the analysis we gain efficiency and, mostly, clarity for interpretation. Multinomial logistic regression analysis was performed to determine the degree of association between the variables being studied. The odds ratio and their 95% confidence intervals, and the receiver operating characteristic (ROC) curve were calculated. The analysis based on the ROC curves is a statistical method to determine the diagnostic preciseness of tests that use continuous scales, and are used for three specific purposes: to establish the cut-off point at which the highest sensitivity and specificity is reached; evaluate the discriminative capacity of the diagnostic test, i.e., its capacity to differentiate healthy and sick individuals; and to compare the discriminative capacity of two or more diagnostic tests that express their results as continuous scales.

In order to verify that emotional intelligence is associated with happiness, multinomial logistic regression analysis included happiness as a predictor variable, grouped according to a criterion of percentiles in low, medium, and high happiness and the emotional intelligence dimensions attention, clarity, and repair as predictor variables, grouped in three categories ( Table 1 ). Gender and age of participants were included as control variables.

Categorization and frequencies of the study variables and descriptive statistics of the OHQ-SF questionnaire.

M = mean, SD = standard deviation. P = Percentile.

Both multinomial regression analyses demonstrated a satisfactory fit, χ 2 (16, N = 629) = 104.922, p < 0.001 (two-tailed), ϕ = 0.048; R Nagelkerke = 0.181, enabling correct classification in 62% of the cases.

The detailed analysis of the findings according to the different emotional intelligence dimensions shows the association between happiness and perceived intra-personal emotional intelligence, so that as clarity and repair increase, the individuals see themselves as happier, and as they decrease the individuals are less happy.

To be precise, for the result of the model with the reference category happiness ( Table 2 ), the calculations of the parameters reveal that adequate clarity (Wald = 4.205, p = 0.040), adequate repair (Wald = 8.609, p = 0.003), adequate repair (Wald = 14.759, p < 0.001), and excellent repair (Wald = 8.503, p =0.004) are associated significantly and directly with medium happiness. In addition, adequate (Wald = 10.376, p = 0.001) and excellent clarity (Wald = 8.610, p = 0.003), and adequate (Wald = 15.997, p < 0.001) and excellent repair (Wald = 25.323, p < 0.001) are correlated directly and significantly with high happiness.

Multinomial logistic regression model examining the probability of perceiving low happiness according to the degree of emotional attention, clarity, and repair.

Reference categories: 1 Low happiness. Groups compared: 2 little attention: 3 should improve clarity; 4 should improve repair. * p < 0.05. OR: odds ratio. CI: confidence interval.

The OR calculations of the model with the reference category low happiness ( Table 2 ) show that the probability of medium happiness is twice as high among individuals with adequate clarity, 3.4 times higher with excellent repair and 2.5 times higher with adequate repair. Similarly, the probability of high happiness is 2.7 times higher with adequate clarity, 4.1 times higher with adequate repair, 5.6 times higher with excellent clarity, and 12 times higher with excellent repair.

In addition, calculations of the parameters for the reference category high happiness ( Table 2 ) reveal that the need to improve clarity (Wald = 8.610, p = 0.003), repair (Wald = 25.323, p < 0.001), and adequate repair (Wald = 6.281, p = 0.012) are associated significantly and directly with low happiness. Equally, the need to improve clarity (Wald = 9.771, p = 0.002) and repair (Wald = 11.861, p = 0.001), and adequate clarity (Wald = 7.082, p = 0.008) and repair (Wald = 8.358, p = 0.004) are correlated directly and significantly with medium happiness.

The OR calculations of the model with the reference category high happiness ( Table 3 ) show that the probability of low happiness is 5.6 times higher among individuals who should improve clarity, 12 times higher among those who should improve repair and 3 times higher with adequate repair. Similarly, the probability of medium happiness is 3.5 times higher among individuals who should improve clarity and repair, 2.6 times higher with adequate clarity, and 2.2 times higher with adequate repair.

Multinomial logistic regression model examining the probability of perceiving high happiness according to the degree of emotional attention, clarity, and repair.

Reference categories: 1 High happiness. Groups compared: 2 Excessive attention; 3 Excellent clarity; 4 Excellent repair. * p < 0.05. OR odds ratio. CI confidence interval.

In addition, a receiver operating characteristic (ROC) curve was analyzed to assess the discriminative accuracy of the emotional intelligence dimensions. This allowed the identification of the cut-out points of the emotional intelligence scores beyond which high happiness becomes more likely.

In the ROC analysis, in the non-parametric case, the curve of the clarity dimension has an area below it of 0.696, 95% CI (0.644, 0.748), p < 0.001, and the repair dimension has below it an area of 0.707, 95% CI (0.656, 0.758), p < 0.001, while in the case of the attention dimension, the area below the curve of 0.536, 95% CI (0.478, 0.595), p = 0.206, does not provide significant information ( Figure 1 ).

ROC curve for the TMMS-24 dimensions predicting the presence of high happiness.

The cut-off points that simultaneously optimize sensitivity and specificity, and the separate cut-off points that optimize sensitivity and specificity of the clarity and repair dimensions are shown in Table 4 .

Sensitivity, specificity and Youden Index for the scores of the clarity and repair dimensions in the TMMS-24.

*** Score that maximizes sensitivity and specificity at the same time. * Score that maximizes sensitivity. ** Score that maximizes specificity.

To identify high happiness, a score of 28.5 or over in the clarity dimension simultaneously maximizes sensitivity (60%) and specificity (71%) (Youden Index = 0.314). A score of 25.5 maximizes sensitivity (72%) while specificity remains higher than expected by random, and a cut-off point of 29.5 maximizes specificity (77%) while sensitivity remains higher than expected by random ( Table 4 ). Similarly, a point of 27.5 or over in the repair dimension simultaneously maximizes sensitivity (78%) and specificity (55%) (Youden Index = 0.333). A score of 26.5 maximizes sensitivity (79%) while specificity remains higher than expected by random, and a cut-off point of 32 maximizes specificity (76%) while sensitivity remains higher than expected by random ( Table 4 ).

4. Discussion

The present study has aimed to analyze the relationship between the dimensions of emotional intelligence (attention, clarity, and repair) and happiness in a sample of adolescents and identify the cut-off points in the emotional intelligence scores, above which high happiness is more likely.

The detailed analysis of the results demonstrates a clear association between emotional intelligence and happiness. In general, these results agree with other research analyzing the association between emotional intelligence and happiness [ 46 , 56 ] or variables connected with it, such as personal and social adjustment [ 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. To be precise, our results show that as emotional clarity and repair increase the individuals perceive themselves to be happier, and when they decrease they are less happy. No association has been found with the attention dimension. They agree with studies on adolescent populations that have found correlations between emotional clarity and repair, but not emotional attention, and variables closely related to happiness, such as well-being and psychological health [ 57 , 58 , 59 ] and quality of life [ 60 ].

This positive relation between happiness and emotional clarity and repair factors show that both abilities are indicators of a better emotional adjustment in adolescents [ 61 , 62 , 63 ]. Thus, the scores for clarity and repair above which happiness is maximized are situated within the established ranges for adequate emotional clarity and repair [ 55 ]. The results underscore that emotional repair has a greater association with happiness. In this line, several researchers have noted that the repair of emotions is fundamental for appropriate psychological functioning and mental health [ 64 , 65 , 66 , 67 ]. Adolescents with higher levels of emotional repair tend to carry out pleasant distracting activities, which can contribute to a greater feeling of happiness [ 68 ].

However, the question is: why is emotional attention not related to happiness? Although emotional attention is necessary for adaptation, paying too much attention to emotions is usually associated with maladaptive factors incompatible with happiness, such as anxiety, depression, hypervigilance, rumination, and catastrophization [ 32 , 33 , 51 ]. Therefore, from this point of view, excessive attention must be associated with low happiness. In contrast, emotional attention implies being aware of the feelings that produce pleasure (happiness) or discomfort (unhappiness). All emotions have a positive function and situations that cause discomfort are inevitable. Therefore, happiness cannot depend on their absence, but on a balance between the quantity and intensity of pleasant/unpleasant. In such a way, people who pay too much attention to their emotions and moods and do not have an adequate emotional clarity and repair would not be capable enough to understand and regulate the different emotional states [ 69 , 70 , 71 , 72 ].

Study Limitations

This was a transversal study; therefore, causal associations cannot be made. Likewise, the sample used and its size restricts generalizability of results. In addition, on the one hand, using the perceptions that the individuals have of their own capacities and feelings hinders the possibility of controlling possible respondent bias. It would therefore be useful to combine their own replies to the questionnaire with tests that are able to evaluate real aptitudes to solve emotional problems. On the other, although the criterion of assigning percentiles to the groups of high, medium, and low happiness allows comparisons to be made between happier or less happy individuals, it does not guarantee the identification of the happy and unhappy individuals, and consequently the results should be interpreted with a degree of caution. Despite these limitations, this study makes interesting contributions to understanding the association between emotional intelligence and happiness.

5. Conclusions

The conclusions of the present study support the idea that some capacities may help to increase the attainment of health and emotional well-being during adolescence. More precisely, it has shown that as adolescents’ capacities of comprehension and emotional regulation increase, so does their subjective happiness. The important role of emotional regulation should be stressed because it is an additional factor associated with happiness.

Finally, we are aware that the educational context is the best setting in which to establish policies promoting emotional health and well-being that can reach all the students and put an end to possible inequalities in the learning of those resources. This study has attempted to determine the specific dimensions that should be focused on when teaching emotional capacities as a variable promoting happiness and emotional well-being and health during this key period of life. To be exact, the capacities of understanding and regulating emotions can be developed and increased in adolescents as a way for their perception of their own happiness to increase.

Author Contributions

J.G.-B., B.L.-d.B., and S.M.-L. designed the study and they had full access to all the data in the study. B.L.-d.B. and S.M.-L. performed all statistical analyses and the interpretation of the data. J.G.-B., B.L.-d.B., R.Y.-T., V.M.L.-R., and S.M.-L. took part in the conduct of the survey and contributed to manuscript preparation. All authors have read and approved the final manuscript.

This work has been funded by the support to Consolidation of Research Groups (Junta de Extremadura GR18091/18.HJ.11). The authors would like to thank their support.

Conflicts of Interest

The authors declare no conflict of interest.