difference research and study

Study vs. Research — What's the Difference?

Difference Between Study and Research

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Study designs: Part 1 – An overview and classification

Priya ranganathan.

Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India

Rakesh Aggarwal

1 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

There are several types of research study designs, each with its inherent strengths and flaws. The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on “study designs,” we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.

INTRODUCTION

Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem.

Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the nature of question, the goal of research, and the availability of resources. Since the design of a study can affect the validity of its results, it is important to understand the different types of study designs and their strengths and limitations.

There are some terms that are used frequently while classifying study designs which are described in the following sections.

A variable represents a measurable attribute that varies across study units, for example, individual participants in a study, or at times even when measured in an individual person over time. Some examples of variables include age, sex, weight, height, health status, alive/dead, diseased/healthy, annual income, smoking yes/no, and treated/untreated.

Exposure (or intervention) and outcome variables

A large proportion of research studies assess the relationship between two variables. Here, the question is whether one variable is associated with or responsible for change in the value of the other variable. Exposure (or intervention) refers to the risk factor whose effect is being studied. It is also referred to as the independent or the predictor variable. The outcome (or predicted or dependent) variable develops as a consequence of the exposure (or intervention). Typically, the term “exposure” is used when the “causative” variable is naturally determined (as in observational studies – examples include age, sex, smoking, and educational status), and the term “intervention” is preferred where the researcher assigns some or all participants to receive a particular treatment for the purpose of the study (experimental studies – e.g., administration of a drug). If a drug had been started in some individuals but not in the others, before the study started, this counts as exposure, and not as intervention – since the drug was not started specifically for the study.

Observational versus interventional (or experimental) studies

Observational studies are those where the researcher is documenting a naturally occurring relationship between the exposure and the outcome that he/she is studying. The researcher does not do any active intervention in any individual, and the exposure has already been decided naturally or by some other factor. For example, looking at the incidence of lung cancer in smokers versus nonsmokers, or comparing the antenatal dietary habits of mothers with normal and low-birth babies. In these studies, the investigator did not play any role in determining the smoking or dietary habit in individuals.

For an exposure to determine the outcome, it must precede the latter. Any variable that occurs simultaneously with or following the outcome cannot be causative, and hence is not considered as an “exposure.”

Observational studies can be either descriptive (nonanalytical) or analytical (inferential) – this is discussed later in this article.

Interventional studies are experiments where the researcher actively performs an intervention in some or all members of a group of participants. This intervention could take many forms – for example, administration of a drug or vaccine, performance of a diagnostic or therapeutic procedure, and introduction of an educational tool. For example, a study could randomly assign persons to receive aspirin or placebo for a specific duration and assess the effect on the risk of developing cerebrovascular events.

Descriptive versus analytical studies

Descriptive (or nonanalytical) studies, as the name suggests, merely try to describe the data on one or more characteristics of a group of individuals. These do not try to answer questions or establish relationships between variables. Examples of descriptive studies include case reports, case series, and cross-sectional surveys (please note that cross-sectional surveys may be analytical studies as well – this will be discussed in the next article in this series). Examples of descriptive studies include a survey of dietary habits among pregnant women or a case series of patients with an unusual reaction to a drug.

Analytical studies attempt to test a hypothesis and establish causal relationships between variables. In these studies, the researcher assesses the effect of an exposure (or intervention) on an outcome. As described earlier, analytical studies can be observational (if the exposure is naturally determined) or interventional (if the researcher actively administers the intervention).

Directionality of study designs

Based on the direction of inquiry, study designs may be classified as forward-direction or backward-direction. In forward-direction studies, the researcher starts with determining the exposure to a risk factor and then assesses whether the outcome occurs at a future time point. This design is known as a cohort study. For example, a researcher can follow a group of smokers and a group of nonsmokers to determine the incidence of lung cancer in each. In backward-direction studies, the researcher begins by determining whether the outcome is present (cases vs. noncases [also called controls]) and then traces the presence of prior exposure to a risk factor. These are known as case–control studies. For example, a researcher identifies a group of normal-weight babies and a group of low-birth weight babies and then asks the mothers about their dietary habits during the index pregnancy.

Prospective versus retrospective study designs

The terms “prospective” and “retrospective” refer to the timing of the research in relation to the development of the outcome. In retrospective studies, the outcome of interest has already occurred (or not occurred – e.g., in controls) in each individual by the time s/he is enrolled, and the data are collected either from records or by asking participants to recall exposures. There is no follow-up of participants. By contrast, in prospective studies, the outcome (and sometimes even the exposure or intervention) has not occurred when the study starts and participants are followed up over a period of time to determine the occurrence of outcomes. Typically, most cohort studies are prospective studies (though there may be retrospective cohorts), whereas case–control studies are retrospective studies. An interventional study has to be, by definition, a prospective study since the investigator determines the exposure for each study participant and then follows them to observe outcomes.

The terms “prospective” versus “retrospective” studies can be confusing. Let us think of an investigator who starts a case–control study. To him/her, the process of enrolling cases and controls over a period of several months appears prospective. Hence, the use of these terms is best avoided. Or, at the very least, one must be clear that the terms relate to work flow for each individual study participant, and not to the study as a whole.

Classification of study designs

Figure 1 depicts a simple classification of research study designs. The Centre for Evidence-based Medicine has put forward a useful three-point algorithm which can help determine the design of a research study from its methods section:[ 1 ]

Classification of research study designs

• Does the study describe the characteristics of a sample or does it attempt to analyze (or draw inferences about) the relationship between two variables? – If no, then it is a descriptive study, and if yes, it is an analytical (inferential) study
• If analytical, did the investigator determine the exposure? – If no, it is an observational study, and if yes, it is an experimental study
• If observational, when was the outcome determined? – at the start of the study (case–control study), at the end of a period of follow-up (cohort study), or simultaneously (cross sectional).

In the next few pieces in the series, we will discuss various study designs in greater detail.

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An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas

Study designs are the set of methods and procedures used to collect and analyze data in a study.

Broadly speaking, there are 2 types of study designs: descriptive studies and analytical studies.

Descriptive studies

• Describes specific characteristics in a population of interest
• The most common forms are case reports and case series
• In a case report, we discuss our experience with the patient’s symptoms, signs, diagnosis, and treatment
• In a case series, several patients with similar experiences are grouped.

Analytical Studies

Analytical studies are of 2 types: observational and experimental.

Observational studies are studies that we conduct without any intervention or experiment. In those studies, we purely observe the outcomes.  On the other hand, in experimental studies, we conduct experiments and interventions.

Observational studies

Observational studies include many subtypes. Below, I will discuss the most common designs.

Cross-sectional study:

• This design is transverse where we take a specific sample at a specific time without any follow-up
• It allows us to calculate the frequency of disease ( p revalence ) or the frequency of a risk factor
• This design is easy to conduct
• For example – if we want to know the prevalence of migraine in a population, we can conduct a cross-sectional study whereby we take a sample from the population and calculate the number of patients with migraine headaches.

Cohort study:

• We conduct this study by comparing two samples from the population: one sample with a risk factor while the other lacks this risk factor
• It shows us the risk of developing the disease in individuals with the risk factor compared to those without the risk factor ( RR = relative risk )
• Prospective : we follow the individuals in the future to know who will develop the disease
• Retrospective : we look to the past to know who developed the disease (e.g. using medical records)
• This design is the strongest among the observational studies
• For example – to find out the relative risk of developing chronic obstructive pulmonary disease (COPD) among smokers, we take a sample including smokers and non-smokers. Then, we calculate the number of individuals with COPD among both.

Case-Control Study:

• We conduct this study by comparing 2 groups: one group with the disease (cases) and another group without the disease (controls)
• This design is always retrospective
•  We aim to find out the odds of having a risk factor or an exposure if an individual has a specific disease (Odds ratio)
•  Relatively easy to conduct
• For example – we want to study the odds of being a smoker among hypertensive patients compared to normotensive ones. To do so, we choose a group of patients diagnosed with hypertension and another group that serves as the control (normal blood pressure). Then we study their smoking history to find out if there is a correlation.

Experimental Studies

• Also known as interventional studies
• Can involve animals and humans
• Pre-clinical trials involve animals
• Clinical trials are experimental studies involving humans
• In clinical trials, we study the effect of an intervention compared to another intervention or placebo. As an example, I have listed the four phases of a drug trial:

I:  We aim to assess the safety of the drug ( is it safe ? )

II: We aim to assess the efficacy of the drug ( does it work ? )

III: We want to know if this drug is better than the old treatment ( is it better ? )

IV: We follow-up to detect long-term side effects ( can it stay in the market ? )

• In randomized controlled trials, one group of participants receives the control, while the other receives the tested drug/intervention. Those studies are the best way to evaluate the efficacy of a treatment.

Finally, the figure below will help you with your understanding of different types of study designs.

References (pdf)

You may also be interested in the following blogs for further reading:

An introduction to randomized controlled trials

Case-control and cohort studies: a brief overview

Cohort studies: prospective and retrospective designs

Prevalence vs Incidence: what is the difference?

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No Comments on An introduction to different types of study design

you are amazing one!! if I get you I’m working with you! I’m student from Ethiopian higher education. health sciences student

Very informative and easy understandable

You are my kind of doctor. Do not lose sight of your objective.

Wow very erll explained and easy to understand

I’m Khamisu Habibu community health officer student from Abubakar Tafawa Balewa university teaching hospital Bauchi, Nigeria, I really appreciate your write up and you have make it clear for the learner. thank you

well understood,thank you so much

Well understood…thanks

Simply explained. Thank You.

Thanks a lot for this nice informative article which help me to understand different study designs that I felt difficult before

That’s lovely to hear, Mona, thank you for letting the author know how useful this was. If there are any other particular topics you think would be useful to you, and are not already on the website, please do let us know.

it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

Thanks for this information

Thanks so much for this information….I have clearly known the types of study design Thanks

That’s so good to hear, Mirembe, thank you for letting the author know.

Very helpful article!! U have simplified everything for easy understanding

I’m a health science major currently taking statistics for health care workers…this is a challenging class…thanks for the simified feedback.

That’s good to hear this has helped you. Hopefully you will find some of the other blogs useful too. If you see any topics that are missing from the website, please do let us know!

Hello. I liked your presentation, the fact that you ranked them clearly is very helpful to understand for people like me who is a novelist researcher. However, I was expecting to read much more about the Experimental studies. So please direct me if you already have or will one day. Thank you

Dear Ay. My sincere apologies for not responding to your comment sooner. You may find it useful to filter the blogs by the topic of ‘Study design and research methods’ – here is a link to that filter: https://s4be.cochrane.org/blog/topic/study-design/ This will cover more detail about experimental studies. Or have a look on our library page for further resources there – you’ll find that on the ‘Resources’ drop down from the home page.

However, if there are specific things you feel you would like to learn about experimental studies, that are missing from the website, it would be great if you could let me know too. Thank you, and best of luck. Emma

Great job Mr Hadi. I advise you to prepare and study for the Australian Medical Board Exams as soon as you finish your undergrad study in Lebanon. Good luck and hope we can meet sometime in the future. Regards ;)

You have give a good explaination of what am looking for. However, references am not sure of where to get them from.

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Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

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Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

Research design is a plan to answer your research question.  A research method is a strategy used to implement that plan.  Research design and methods are different but closely related, because good research design ensures that the data you obtain will help you answer your research question more effectively.

Which research method should I choose ?

It depends on your research goal.  It depends on what subjects (and who) you want to study.  Let's say you are interested in studying what makes people happy, or why some students are more conscious about recycling on campus.  To answer these questions, you need to make a decision about how to collect your data.  Most frequently used methods include:

• Observation / Participant Observation
• Focus Groups
• Experiments
• Secondary Data Analysis / Archival Study
• Mixed Methods (combination of some of the above)

One particular method could be better suited to your research goal than others, because the data you collect from different methods will be different in quality and quantity.   For instance, surveys are usually designed to produce relatively short answers, rather than the extensive responses expected in qualitative interviews.

What other factors should I consider when choosing one method over another?

Time for data collection and analysis is something you want to consider.  An observation or interview method, so-called qualitative approach, helps you collect richer information, but it takes time.  Using a survey helps you collect more data quickly, yet it may lack details.  So, you will need to consider the time you have for research and the balance between strengths and weaknesses associated with each method (e.g., qualitative vs. quantitative).

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Q. What's the difference between a research article (or research study) and a review article?

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Answered By: Priscilla Coulter Last Updated: Jul 29, 2022     Views: 232416

A research paper is a primary source ...that is, it reports the methods and results of an original study performed by the authors . The kind of study may vary (it could have been an experiment, survey, interview, etc.), but in all cases, raw data have been collected and analyzed by the authors , and conclusions drawn from the results of that analysis.

Research papers follow a particular format.  Look for:

• A brief introduction will often include a review of the existing literature on the topic studied, and explain the rationale of the author's study.  This is important because it demonstrates that the authors are aware of existing studies, and are planning to contribute to this existing body of research in a meaningful way (that is, they're not just doing what others have already done).
• A methods section, where authors describe how they collected and analyzed data.  Statistical analyses are included.  This section is quite detailed, as it's important that other researchers be able to verify and/or replicate these methods.
• A results section describes the outcomes of the data analysis.  Charts and graphs illustrating the results are typically included.
• In the discussion , authors will explain their interpretation of their results and theorize on their importance to existing and future research.
• References or works cited are always included.  These are the articles and books that the authors drew upon to plan their study and to support their discussion.

You can use the library's article databases to search for research articles:

• A research article will nearly always be published in a peer-reviewed journal; click here for instructions on limiting your searches to peer-reviewed articles.  
• If you have a particular type of study in mind, you can include keywords to describe it in your search .  For instance, if you would like to see studies that used surveys to collect data, you can add "survey" to your topic in the database's search box. See this example search in our EBSCO databases: " bullying and survey ".   
• Several of our databases have special limiting options that allow you to select specific methodologies.  See, for instance, the " Methodology " box in ProQuest's PsycARTICLES Advanced Search (scroll down a bit to see it).  It includes options like "Empirical Study" and "Qualitative Study", among many others.  

A review article is a secondary source ...it is written about other articles, and does not report original research of its own.  Review articles are very important, as they draw upon the articles that they review to suggest new research directions, to strengthen support for existing theories and/or identify patterns among exising research studies.  For student researchers, review articles provide a great overview of the existing literature on a topic.    If you find a literature review that fits your topic, take a look at its references/works cited list for leads on other relevant articles and books!

You can use the library's article databases to find literature reviews as well!  Click here for tips.

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Research vs. study

The confusion about these words is that they can both be either nouns or verbs. If you ask someone, "Does 'studies' mean the same as 'researches'?" you may hear "Yes," but it is only true if they are used as verbs. As nouns, they have subtly different meanings.

"This team has done a lot of good research. I just read their latest study, which they wrote about calcium in germinating soybeans. It described several interesting experiments."

research 1. to perform a systematic investigation

1. "What kind of scientist is he? He's a botanist. He researches plants."

study 1. to perform a systematic investigation; 2. to actively learn or memorize academic material

1. "What kind of scientist is he? He's a botanist. He studies plants."

2. "Mindy studies every day. That is why she gets such excellent grades. She wants to go to college to study math."

Some authors say "research" when they mean "study." "Research," as a verb, means "to perform a study or studies," but "research" as a noun refers to the sum of many studies. "Chemical research" means the sum of all chemical studies. If you find yourself writing "a research" or "in this research," change it to "a study" or "in this study."

research The act of performing research. Also, the results of research. Note that "research" is a mass noun. It is already plural in meaning but grammatically singular. If you want to indicate more than one type, say "bodies of research" or "pieces of research," not "researches."

"Dr. Lee was a prolific scientist. She performed a great deal of research over her long career."

study A single research project or paper.

"Dr. Lee was a prolific scientist. She performed a great many studies over her long career."

The noun "study" refers to a single paper or project. You can replace "paper" with "study" in almost all cases (but not always the other way around), to the point where you can say "I wrote a study." The noun "research" means more like a whole body of research including many individual studies: The research of a field. The lifetime achievements of a scientist or research team.

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Case Study vs. Research

What's the difference.

Case study and research are both methods used in academic and professional settings to gather information and gain insights. However, they differ in their approach and purpose. A case study is an in-depth analysis of a specific individual, group, or situation, aiming to understand the unique characteristics and dynamics involved. It often involves qualitative data collection methods such as interviews, observations, and document analysis. On the other hand, research is a systematic investigation conducted to generate new knowledge or validate existing theories. It typically involves a larger sample size and employs quantitative data collection methods such as surveys, experiments, or statistical analysis. While case studies provide detailed and context-specific information, research aims to generalize findings to a broader population.

Further Detail

Introduction.

When it comes to conducting studies and gathering information, researchers have various methods at their disposal. Two commonly used approaches are case study and research. While both methods aim to explore and understand a particular subject, they differ in their approach, scope, and the type of data they collect. In this article, we will delve into the attributes of case study and research, highlighting their similarities and differences.

A case study is an in-depth analysis of a specific individual, group, event, or phenomenon. It involves a detailed examination of a particular case to gain insights into its unique characteristics, context, and dynamics. Case studies often employ multiple sources of data, such as interviews, observations, and documents, to provide a comprehensive understanding of the subject under investigation.

One of the key attributes of a case study is its focus on a specific case, which allows researchers to explore complex and nuanced aspects of the subject. By examining a single case in detail, researchers can uncover rich and detailed information that may not be possible with broader research methods. Case studies are particularly useful when studying rare or unique phenomena, as they provide an opportunity to deeply analyze and understand them.

Furthermore, case studies often employ qualitative research methods, emphasizing the collection of non-numerical data. This qualitative approach allows researchers to capture the subjective experiences, perspectives, and motivations of the individuals or groups involved in the case. By using open-ended interviews and observations, researchers can gather rich and detailed data that provides a holistic view of the subject.

However, it is important to note that case studies have limitations. Due to their focus on a specific case, the findings may not be easily generalized to a larger population or context. The small sample size and unique characteristics of the case may limit the generalizability of the results. Additionally, the subjective nature of qualitative data collection in case studies may introduce bias or interpretation challenges.

Research, on the other hand, is a systematic investigation aimed at discovering new knowledge or validating existing theories. It involves the collection, analysis, and interpretation of data to answer research questions or test hypotheses. Research can be conducted using various methods, including surveys, experiments, and statistical analysis, depending on the nature of the study.

One of the primary attributes of research is its emphasis on generating generalizable knowledge. By using representative samples and statistical techniques, researchers aim to draw conclusions that can be applied to a larger population or context. This allows for the identification of patterns, trends, and relationships that can inform theories, policies, or practices.

Research often employs quantitative methods, focusing on the collection of numerical data that can be analyzed using statistical techniques. Surveys, experiments, and statistical analysis allow researchers to measure variables, establish correlations, and test hypotheses. This objective approach provides a level of objectivity and replicability that is crucial for scientific inquiry.

However, research also has its limitations. The focus on generalizability may sometimes sacrifice the depth and richness of understanding that case studies offer. The reliance on quantitative data may overlook important qualitative aspects of the subject, such as individual experiences or contextual factors. Additionally, the controlled nature of research settings may not fully capture the complexity and dynamics of real-world situations.

Similarities

Despite their differences, case studies and research share some common attributes. Both methods aim to gather information and generate knowledge about a particular subject. They require careful planning, data collection, analysis, and interpretation. Both case studies and research contribute to the advancement of knowledge in their respective fields.

Furthermore, both case studies and research can be used in various disciplines, including social sciences, psychology, business, and healthcare. They provide valuable insights and contribute to evidence-based decision-making. Whether it is understanding the impact of a new treatment, exploring consumer behavior, or investigating social phenomena, both case studies and research play a crucial role in expanding our understanding of the world.

In conclusion, case study and research are two distinct yet valuable approaches to studying and understanding a subject. Case studies offer an in-depth analysis of a specific case, providing rich and detailed information that may not be possible with broader research methods. On the other hand, research aims to generate generalizable knowledge by using representative samples and quantitative methods. While case studies emphasize qualitative data collection, research focuses on quantitative analysis. Both methods have their strengths and limitations, and their choice depends on the research objectives, scope, and context. By utilizing the appropriate method, researchers can gain valuable insights and contribute to the advancement of knowledge in their respective fields.

Comparisons may contain inaccurate information about people, places, or facts. Please report any issues.

Designing Difference in Difference Studies: Best Practices for Public Health Policy Research

Affiliations.

• 1 School of Public and Environmental Affairs, Indiana University, Bloomington, Indiana 47405, USA; email: [email protected] , [email protected].
• 2 School of Public and Environmental Affairs, Indiana University, Bloomington, Indiana 47405, USA, and National Bureau of Economic Research; email: [email protected].
• PMID: 29328877
• DOI: 10.1146/annurev-publhealth-040617-013507

The difference in difference (DID) design is a quasi-experimental research design that researchers often use to study causal relationships in public health settings where randomized controlled trials (RCTs) are infeasible or unethical. However, causal inference poses many challenges in DID designs. In this article, we review key features of DID designs with an emphasis on public health policy research. Contemporary researchers should take an active approach to the design of DID studies, seeking to construct comparison groups, sensitivity analyses, and robustness checks that help validate the method's assumptions. We explain the key assumptions of the design and discuss analytic tactics, supplementary analysis, and approaches to statistical inference that are often important in applied research. The DID design is not a perfect substitute for randomized experiments, but it often represents a feasible way to learn about casual relationships. We conclude by noting that combining elements from multiple quasi-experimental techniques may be important in the next wave of innovations to the DID approach.

Keywords: causal inference; difference in difference; policy analysis; quasi-experiments; research design.

• Data Interpretation, Statistical
• Health Policy*
• Policy Making*
• Public Health*
• Public Policy
• Research Design*

Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis.

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

Related Articles

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Qualitative Data Coding

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What Is Internal Validity In Research?

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Criterion Validity: Definition & Examples

Purdue HHS students recognized with highly competitive awards for research/study abroad

Written by: Denise Buhrmester, [email protected]

Nine Purdue University College of Health and Human Sciences students were recently recognized by the National and International Scholarship Office in the John Martinson Honors College for receiving highly selective external awards requiring a campus nomination from Purdue.

• Samuel Gray (psychological sciences major) — Fulbright U.S. Student Program Alternate*
• Victoria Patellos (psychological sciences major) — Fulbright U.S. Student Program Grant Offered
• Deirdre Sullivan (psychological sciences major) — Fulbright U.S. Student Program Alternate*
• Lydia Farmer (psychological sciences and developmental and family science majors) — Gilman Scholarship Recipient
• Marina Haworth-Snow (medical laboratory sciences major) — Gilman Scholarship Recipient
• Madison Koenig (family and consumer sciences education major) — Gilman Scholarship Recipient
• Autumn Reynolds (public health major) — Gilman Scholarship Recipient
• Nicholas Walters (kinesiology major) — Gilman Scholarship Recipient
• Alanna Patterson (early childhood education and exceptional needs major) — Gilman-McCain Scholarship Recipient

Fullbright U.S. Student Program

The Fulbright U.S. Student Program provides postbaccalaureate funding for eight to 12 months for individuals to study, research or teach abroad while promoting cultural exchange and mutual understanding. Eligible applicants include undergraduates entering their senior year, alumni who earned a bachelor’s or master’s degree from Purdue as their most recent degree, and current graduate students who will not have completed a PhD prior to the beginning of their grant year.

There are two major types of Fulbright awards for U.S. students: study/research grants and English teaching assistantship grants .

Gilman Scholarship

The Gilman Scholarship Program broadens the student population that studies and interns abroad by supporting undergraduates who might not otherwise participate due to financial constraints.  The program also aims to encourage students to study and intern in a diverse array of countries and world regions.

Award amounts vary depending on the length of study and student need. Applicants who are studying  a critical-need language  while abroad in a country in which the language is predominately spoken may be considered for the Critical Need Language Award for a total maximum award of $8,000.

New:  In 2023, Gilman now offers a STEM Supplemental Award of up to $1,000 to conduct STEM research associated with the student’s study abroad program.

Gilman-McCain Scholarship

The  Gilman-McCain Scholarship is a congressionally funded initiative of the Bureau of Educational and Cultural Affairs at the U.S. Department of State and named after the late senator John S. McCain from Arizona. The Gilman-McCain Scholarship provides awards of $5,000 for child dependents of active duty service members to study or intern abroad on credit-bearing programs.

*Fulbright Alternate: A candidate who can be promoted to finalist status if additional funding becomes available.

Cancer patients often do better with less intensive treatment, research shows

Scaling back treatment for three kinds of cancer can make life easier for patients without compromising outcomes, doctors reported at the world’s largest cancer conference .

It’s part of a long-term trend toward studying whether doing less — less surgery, less chemotherapy or less radiation — can help patients live longer and feel better. The latest studies involved ovarian and esophageal cancer and Hodgkin lymphoma.

Thirty years ago, cancer research was about doing more, not less. In one sobering example, women with advanced breast cancer were pushed to the brink of death with massive doses of chemotherapy and bone marrow transplants. The  approach didn’t work  any better than chemotherapy and patients suffered.

Now, in a quest to optimize cancer care, researchers are asking: “Do we need all that treatment that we have used in the past?”

It’s a question, “that should be asked over and over again,” said Dr. Tatjana Kolevska, medical director for the Kaiser Permanente National Cancer Excellence Program, who was not involved in the new research.

Often, doing less works because of improved drugs.

“The good news is that cancer treatment is not only becoming more effective, it’s becoming easier to tolerate and associated with less short-term and long-term complications,” said Dr. William G. Nelson of Johns Hopkins School of Medicine, who was also not involved in the new research.

Latest news on cancer treatment

• Cancer-fighting antibodies inject chemo directly into tumor cells, upping effectiveness.
• Long-term study shows 'remarkable' treatment helps patients with deadly nonsmoking-related lung cancer.
• FDA approves groundbreaking treatment for advanced melanoma.

Studies demonstrating the trend were discussed over the weekend at an American Society of Clinical Oncology conference in Chicago. Here are the highlights:

Ovarian cancer

French researchers found that it’s safe to avoid removing lymph nodes that appear healthy during surgery for advanced ovarian cancer. The study compared the results for 379 patients — half had their lymph nodes removed and half did not. After nine years, there was no difference in how long the patients lived and those with less-extreme surgery had fewer complications, such as the need for blood transfusions. The research was funded by the National Institute of Cancer in France.

Esophageal cancer

This German study looked at 438 people with a type of cancer of the esophagus that can be treated with surgery. Half received a common treatment plan that included chemotherapy and surgery on the esophagus, the tube that carries food from the throat to the stomach. Half got another approach that includes radiation too. Both techniques are considered standard. Which one patients get can depend on where they get treatment.

After three years, 57% of those who got chemo and surgery were alive, compared to 51% of those who got chemo, surgery and radiation. The German Research Foundation funded the study.

Hodgkin lymphoma

A comparison of two chemotherapy regimens for advanced Hodgkin lymphoma found the less intensive treatment was more effective for the blood cancer and caused fewer side effects.

After four years, the less harsh chemo kept the disease in check in 94% of people, compared to 91% of those who had the more intense treatment. The trial included 1,482 people in nine countries — Germany, Austria, Switzerland, the Netherlands, Denmark, Sweden, Norway, Australia and New Zealand — and was funded by Takeda Oncology, the maker of one of the drugs used in the gentler chemo that was studied.

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Gun Violence Widely Viewed as a Major – and Growing – National Problem

Growing shares of Americans view both gun violence and violent crime as very big national problems. 49% of U.S. adults say gun ownership increases safety by allowing law-abiding citizens to protect themselves; an identical share says it reduces safety by giving too many people access to firearms and increasing misuse.

About 1 in 4 U.S. teachers say their school went into a gun-related lockdown in the last school year

59% of public K-12 teachers say they are at least somewhat worried about the possibility of a shooting ever happening at their school.

For Most U.S. Gun Owners, Protection Is the Main Reason They Own a Gun

Gun owners in the United States continue to cite protection far more than other factors, including hunting and sport shooting, as a major reason they own a gun. About half of Americans who don’t own a gun say they could never see themselves owning one (52%) while nearly as many could imagine themselves as gun owners in the future (47%).

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Striking findings from 2023

Here’s a look back at 2023 through some of our most striking research findings.

Key facts about Americans and guns

Six-in-ten U.S. adults say gun violence is a very big problem in the country today, up 9 percentage points from spring 2022.

What the data says about gun deaths in the U.S.

More Americans died of gun-related injuries in 2021 than in any other year on record, according to the latest available statistics from the CDC.

Gun deaths among U.S. children and teens rose 50% in two years

In 2021, there were 2,590 gun deaths among U.S. children and teens under the age of 18, up from 1,732 in 2019.

About a third of K-12 parents are very or extremely worried a shooting could happen at their children’s school

Mothers are more likely than fathers to be extremely or very worried about a school shooting, and concerns also vary by race and ethnicity.

Republicans in Congress are about three times as likely as Democrats to indicate that they own a gun

At least 110 representatives and senators in the current 117th Congress have publicly stated or otherwise confirmed that they own a gun.

Abortion Rises in Importance as a Voting Issue, Driven by Democrats

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Gen Z is growing up: In 2024, the generation born between 1996 to 2010 is expected to overtake Baby Boomers in the full-time workforce, according to a recent analysis by Glassdoor .

They are bringing to the office a different set of values, behaviors, and expectations than prior generations, according to research by Roberta Katz , a former senior research scholar at Stanford’s Center for Advanced Study in the Behavioral Sciences (CASBS) . Katz collaborated with a team of researchers to conduct a large, multi-year study to find out what matters to Gen Z and why – findings that culminated in a book and website .

Stanford Report sat down with Katz to talk about this research and what to expect from Gen Z in the workplace.

1. Gen Z expects change

The world Gen Zers came of age in was fundamentally different from that of their parents and even millennials, people who were born in the early 1980s to 1996.

The world of Gen Z has been defined by technological changes happening at rapid speeds that also reshaped social experiences. Disruption and impermanence have always been part of the world Gen Z experienced – for them, it’s a norm, not an exception.

“There is an expectation of constant change,” said Katz.

Growing up amid uncertainty has given Gen Z a unique set of characteristics, including being flexible and resilient. It has opened them up to new ways of thinking about the future and doing things – and questioning the ways things are done, which leads to the next trait Gen Zers will bring with them to work.

2. Gen Z is pragmatic

Gen Z has a strong sense of self-agency.

Gen Z lives in a world that has always been one search engine result away. If they want to know more about something, they readily seek the answer out for themselves ( even if it’s not always the correct one ).

They question everything and everyone – from their peers, parents, or people at work. “They don’t necessarily see elders as experts,” Katz said. “They want to understand why something is done in a certain way. They’re very pragmatic.”

They are also not afraid to challenge why things are done the way they are.

“When an older person says to them, ‘This is how you should do it,’ they want to check that out for themselves. It doesn’t mean they’re always right; it’s a different way of understanding,” Katz explained.

3. Gen Z wants to make a difference

Gen Zers not only expect change – they demand it.

They are inheriting a set of complex problems – from climate change to inequality to racial injustice, to name but a few – and want to fix it. They want to work for a place that they believe is doing good in the world.

Some Gen Zers will hold their employers accountable on the causes and issues that matter to them.

Katz warns that for some employers, it can be challenging – if not untenable – to take a position on politically charged or sensitive topics. “It is impossible for most institutions that represent lots of people and lots of identities to satisfy everybody,” Katz said.

4. Gen Z values collaboration and teamwork

For some Gen Zers, the digital world helped shape their identity: Through social media and in online groups, they found subcultures to connect and interact with.

They grew up with wikis – websites collaboratively built and edited by its users – and fandoms – enthusiastic and energetic communities centered around a shared, common interest. For example, K-pop sensation BTS has its Army , Beyonce has her Beyhive, and Taylor Swift has her Swifties.

“They’re in a posse – even with their headphones on,” Katz said.

To get things done, they value collaboration.

“There is a hope that everybody who is contributing is in it for the good of the whole,” Katz describes. “They want to have a team spirit.”

5. Gen Z wants leaders who guide by consensus

Gen Z is also less hierarchical than previous generations.

“They don’t believe in hierarchy for hierarchy’s sake,” Katz said. “They do believe in hierarchy where it is useful.”

Instead, Gen Zers prefer leadership that is dependent on expertise that is task or time specific. That could mean they favor management where team members take turns leading the group (known as a “rotating leadership” model). Another style they may prefer is “collaborative leadership,” in which people from across the organization participate in decision-making and problem-solving.

Transparency is also important.

Gen Zers value consensus and they look for leaders who are in service of the group (also called “service leadership”).

6. Gen Z cares about mental health and work-life balance

Gen Z grew up in a period that saw the blurring of the 9-to-5 work schedule and the rise of flexible work models – a mode of working that led to older generations feeling a pressure to always be “on.”

“Work and home life are all so integrated that if you don’t pay attention, you could be working all the time,” said Katz. “I think Gen Z is sensitive to that.”

Having a work-life balance and maintaining mental and physical health is also important to Gen Z.

“They’re placing a value on the human experience and recognizing that life is more than work,” Katz said.

7. Gen Z thinks differently about loyalty

Because Gen Z grew up amid so much change, Gen Z has a different perspective on loyalty.

But as Katz pointed out, “they also grew up with workplaces not being very loyal to their employees.”

Gen Zers were raised in the shadows of the global financial crisis of 2008, an event that has had long-lasting impacts on employment and the nature of work. “It used to be that people went to work for big companies thinking they’d be there for their entire career and that the company would watch out for them: providing health insurance, and so on,” Katz said.

But after the 2008 recession, and even more recently following the COVID-19 pandemic, companies have cut back labor costs and implemented other cost-saving measures, like reducing perks and benefits. Meanwhile, mass layoffs have also been rampant.

“There’s a reason that employees don’t feel the same degree of loyalty, too,” Katz said.

Meanwhile, the gig economy has also been present throughout Gen Zers’ lives, as has the rise of contract work. They are entrepreneurial, which is part of their pragmatic tendencies.

8. Gen Z looks for trust and authenticity

Gen Z also values authenticity.

“Authenticity is about trust,” Katz said. “Words and actions need to match.”

Honesty and openness are important.

For Katz, it’s all about mutually respectful communication. “My bottom line always to employers is stay open to hearing about different ways to get things done, because Gen Z has one foot in the future.”

Katz is associate vice president for strategic planning, emerita, and is currently involved in a strategic role with the Stanford Doerr School of Sustainability and the Stanford Institute for Human-Centered Artificial Intelligence . She also serves as vice chair of the board of the Center for Advanced Study in the Behavioral Sciences (CASBS).

Katz studied Gen Z as part of a multi-year CASBS research project with Sarah Ogilvie, a linguist at the University of Oxford and formerly at Stanford; Jane Shaw, a historian who is the principal of Harris Manchester College at Oxford and was previously dean for religious life at Stanford; and Linda Woodhead, a sociologist at King’s College London. The research was funded by the Knight Foundation.

From 2004 to 2017, Katz served under Stanford University Presidents John Hennessy and Marc Tessier-Lavigne as associate vice president for strategic planning, and in 2017 as interim chief of staff.

• Introduction
• Conclusions
• Article Information

eTable 1. ICES Data Sources

eTable 2. Operational Definitions of All Variables

eTable 3. Search Strategy for Direct-to-Consumer (Virtual-Only Walk-In Clinic) Group Numbers

eTable 4. Characteristics of Patients in Unmatched Cohort

eTable 5. Top 20 Diagnoses of All Patients in Matched Cohort

eTable 6. Relative Risk of Having an Emergency Department Visit After a Virtual Visit With a Physician Outside Patient Enrolling Group by Age and Rurality Subgroup

eTable 7. Sensitivity Analysis: Characteristics of Patients in the Matched Cohort

eTable 8. Patient Outcomes in the Matched Sensitivity Cohort

eFigure 1. Study Population Flowchart

eFigure 2. Density of Propensity Scores Before and After Matching

eFigure 3. Kaplan-Meier Curve of Time to Emergency Department Visit for Patients With Index Virtual Visit With Own Enrolling Family Physician and Physician Outside Enrolling Group

eFigure 4. Kaplan-Meier Curve of Time to Emergency Department Visit for Patients With Index Virtual Visit With Own Enrolling Physician and Virtual-Only Walk-In Clinic

eReferences.

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Lapointe-Shaw L , Salahub C , Austin PC, et al. Virtual Visits With Own Family Physician vs Outside Family Physician and Emergency Department Use. JAMA Netw Open. 2023;6(12):e2349452. doi:10.1001/jamanetworkopen.2023.49452

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Virtual Visits With Own Family Physician vs Outside Family Physician and Emergency Department Use

• 1 University Health Network, Toronto, Ontario, Canada
• 2 ICES, Toronto, Ontario, Canada
• 3 Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
• 4 Department of Medicine, University of Toronto, Toronto, Ontario, Canada
• 5 Women’s College Institute for Health System Solutions and Virtual Care, Women’s College Hospital, Toronto, Ontario, Canada
• 6 Division of General Internal Medicine and Geriatrics, University Health Network and Sinai Health System, Toronto, Ontario, Canada
• 7 Sunnybrook Research Institute, Toronto, Ontario, Canada
• 8 Department of Cardiology, University Health Network, Toronto, Ontario, Canada
• 9 Patient Partner, Toronto, Ontario, Canada
• 10 Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada
• 11 MAP Centre for Urban Health Solutions, St Michael’s Hospital, Toronto, Ontario, Canada
• 12 Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada
• 13 Department of Family Medicine, Women’s College Hospital, Toronto, Ontario, Canada
• 14 Women’s College Research Institute, Women’s College Hospital, Toronto, Ontario, Canada
• 15 Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada
• 16 Department of Family and Community Medicine, St Michael’s Hospital, Unity Health Toronto, Toronto, Ontario, Canada

Question   Does health care use differ after a virtual visit with a patient’s own family physician compared with a visit with an outside physician?

Findings   In this cohort study among 5 229 240 Ontario residents with a family physician and virtual visit, patients who had a virtual visit with an outside physician were 66% more likely to visit the emergency department within 7 days compared with those with a virtual visit with their own physician.

Meaning   This finding suggests that virtual care with an outside physician is associated with more emergency department visits.

Importance   Virtual visits became more common after the COVID-19 pandemic, but it is unclear in what context they are best used.

Objective   To investigate whether there was a difference in subsequent emergency department use between patients who had a virtual visit with their own family physician vs those who had virtual visits with an outside physician.

Design, Setting, and Participants   This propensity score–matched cohort study was conducted among all Ontario residents attached to a family physician as of April 1, 2021, who had a virtual family physician visit in the subsequent year (to March 31, 2022).

Exposure   The type of virtual family physician visit, with own or outside physician, was determined. In a secondary analysis, own physician visits were compared with visits with a physician working in direct-to-consumer telemedicine.

Main Outcome and Measure   The primary outcome was an emergency department visit within 7 days after the virtual visit.

Results   Among 5 229 240 Ontario residents with a family physician and virtual visit, 4 173 869 patients (79.8%) had a virtual encounter with their own physician (mean [SD] age, 49.3 [21.5] years; 2 420 712 females [58.0%]) and 1 055 371 patients (20.2%) had an encounter with an outside physician (mean [SD] age, 41.8 [20.9] years; 605 614 females [57.4%]). In the matched cohort of 1 885 966 patients, those who saw an outside physician were 66% more likely to visit an emergency department within 7 days than those who had a virtual visit with their own physician (30 748 of 942 983 patients [3.3%] vs 18 519 of 942 983 patients [2.0%]; risk difference, 1.3% [95% CI, 1.2%-1.3%]; relative risk, 1.66 [95% CI, 1.63-1.69]). The increase in the risk of emergency department visits was greater when comparing 30 216 patients with definite direct-to-consumer telemedicine visits with 30 216 patients with own physician visits (risk difference, 4.1% [95% CI, 3.8%-4.5%]; relative risk, 2.99 [95% CI, 2.74-3.27]).

Conclusions and Relevance   In this study, patients whose virtual visit was with an outside physician were more likely to visit an emergency department in the next 7 days than those whose virtual visit was with their own family physician. These findings suggest that primary care virtual visits may be best used within an existing clinical relationship.

The COVID-19 pandemic has fueled the growth of virtual care throughout the health care system. 1 - 7 This has included direct-to-consumer telemedicine (also known as virtual-only walk-in clinics), which typically offers on-demand virtual health care without an option for an in-person exam and is disconnected from a patient’s existing source of primary care. 8 Although some argue that direct-to-consumer telemedicine met a gap in patient care needs, 9 it also amplified tensions between continuity and access to timely or convenient care. 8 Furthermore, the ease of access afforded by on-demand virtual care could drive demand for in-the-moment health care that otherwise would not exist. 10 Some studies have found that direct-to-consumer telemedicine was associated with decreases in health care use by replacing in-person care, 11 , 12 while others suggest that it was associated with increased total use and costs. 13 - 17

Continuity is a cornerstone of good primary care, and numerous studies have demonstrated that high relational continuity is associated with better health outcomes and lower costs. 18 - 22 However, little research has been done to understand differences in subsequent health care use when virtual primary care is provided in the context of an ongoing relationship vs outside of that relationship, as is the case for direct-to-consumer telemedicine.

We used population-based administrative data from Ontario, Canada, to compare subsequent health care use after a virtual visit with a patient’s own family physician vs an outside family physician. We hypothesized that virtual primary care delivered outside an existing relationship would be associated with increased emergency department use.

The use of data in this cohort study is authorized under section 45 of Ontario’s Personal Health Information Protection Act and did not require review by a research ethics board or informed consent. We followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline for cohort studies.

We conducted a population-based, retrospective, propensity score–matched cohort study of residents in Ontario, Canada, who had a virtual visit with a family physician from April 1, 2021, to March 31, 2022. Ontario is Canada’s most populous province, with more than 14.5 million residents. Provincial health insurance is provided to all permanent residents without premiums or copayments and covers emergency department visits, hospitalizations, and all medically necessary physician care. Most primary care is provided by family physicians, and nearly 80% of the population is enrolled to a family physician working in a patient enrollment model. 23 Enrolling practices are expected to meet all primary care needs, including after-hours care. 24 In capitation-based models (the model for 65% of enrolling physicians), there is negation (reduction in an access bonus) when patients of enrolling physicians visit family physicians outside the enrolling group. 25

After the onset of the COVID-19 pandemic in 2020, the Ministry of Health introduced several new, temporary physician billing codes for synchronous virtual visits conducted by video or phone, with a fee equivalent to that of in-person visits. Since then, most publicly funded virtual visits have been conducted by phone. 26 , 27 Asynchronous visits, provided by email or text message, were not covered by provincial insurance.

Population-based health administrative data sets (eTable 1 in Supplement 1 ) were linked using unique encoded identifiers and analyzed at ICES in Ontario, Canada. ICES is an independent, nonprofit research institute whose legal status under Ontario health information privacy law allows the institute to collect and analyze health care and demographic data without consent for health system evaluation and improvement.

We included all Ontarians who were enrolled to a family physician practicing in a patient enrollment model as of April 1, 2021. We excluded individuals with missing Rurality Index of Ontario scores (a measure of urban or rural residence) or neighborhood income quintile information (<1% missing). 28

The exposure (a binary variable) was determined at the first virtual family physician visit (ie, index) that was with the patient’s own enrolling physician or with another physician outside the enrolling group from April 1, 2021, to March 31, 2022. We excluded virtual visits with family physicians who worked in a focused practice or with an emergency medicine subspecialization. 29

We did not include virtual visits with another physician within the same group given that we sought to contrast highest-continuity virtual visits (own physician) with lowest-continuity virtual visits (outside of group). Virtual family physician visits outside the enrolling group likely represented encounters with direct-to-consumer telemedicine clinics (also known as virtual-only walk-in clinics) or combined virtual and in-person walk-in clinics.

The primary outcome was the occurrence of an emergency department visit within 7 days of the index virtual encounter. Secondary outcomes were a low-acuity emergency department visit (defined as a Canadian Triage and Acuity Scale [CTAS] score of 4-5) within 7 days, an emergency department visit within 30 days, a time to emergency department visit within 30 days, and an in-person or virtual visit with any family physician, the same physician, or own enrolling physician within 7 days. Post hoc outcomes were emergency department visits on day 1 and day 2, a high-acuity emergency department visit within 7 days (CTAS score, 1-2), and a tracer outcome of an emergency department visit for a high-acuity (CTAS score, 1-2) motor vehicle accident on days 3 to 30. 30 This tracer was chosen given that the acuity level and separation from the index virtual visit by 3 days made it unlikely that such an event would be a cause or consequence of the type of virtual visit.

We included the following patient characteristics (see operational definitions in eTable 2 in Supplement 1 ): age, sex, urban or rural residence, 31 neighborhood income quintile, and whether the patient was a recent insurance registrant (within the past 10 years), a proxy for recent immigration. 23 We also reported the type of primary care enrollment model, visits to the enrolling physician in the previous 2 years, and the burden of comorbidities using the Johns Hopkins ACG System version 10 resource utilization bands (RUBs), which are based on diagnoses and health care use over the previous 2 years. 32 , 33 Related to the index visit, we measured the diagnosis (top 20 categorized), the calendar quarter, whether the visit was on a weekend, if an after-hours code was claimed, and whether the visit was by phone or video or was missing this information (a required code to identify phone or video was introduced in October 2021).

We first described the cohort using mean (SD), median (IQR), and counts and frequencies. We compared characteristics between groups in unmatched and matched cohorts using standardized mean differences (SMDs), with a difference of 0.1 (10%) or greater considered meaningful. 34

We derived propensity scores using a logistic regression model that included age, sex, neighborhood income quintile, recent immigrant status, count of visits with the patient’s own physician in the previous 2 years, RUB, enrollment model, diagnosis, modality, and whether the visit was on a weekend or after hours on a weekday. Age and count of visits were modeled using restricted cubic splines with 5 knots at the 5th, 27.5th, 50th, 72.5th, and 95th percentile values. 35 We then matched exposure groups 1:1 within a caliper distance of 0.2 × the SD of the logit of the propensity score, 36 while also hard matching on Rurality Index of Ontario category (3 categories: 0-9, large urban; 10-40, small urban; and ≥40, rural) and age group (0-17, 18-64, and ≥65 years).

We reported relative risks (RRs) and risk differences (RDs) for all binary outcomes with 95% CIs, accounting for the paired nature of the matched sample. 37 , 38 For the time-to-event outcome, we reported the hazard ratio obtained using a Cox proportional hazards model with robust variance estimate. 39 Analyses were executed in SAS statistical software version 9.4 (SAS Institute) using a 2-tailed P  < .05 statistical significance threshold.

We report primary outcome results for each hard-matched subgroup: patients residing in large urban, small urban, or rural areas and those aged 0 to 17 years, 18 to 64 years, and 65 years or older. We compared the association between type of virtual visit and our tracer outcome to the e value, 40 which is the minimum strength of association that an unmeasured confounder would need to have with the exposure and the outcome to fully explain the observed exposure-outcome association. 41 , 42

To separately assess outcomes associated with low-continuity virtual visits without the possibility of a physical examination, we compared virtual visits to a patient’s own physician with visits to a known direct-to-consumer telemedicine clinic. We identified these visits using group billing numbers, a method we updated 13 for this study (eTable 3 in Supplement 1 ). Notably, this is a convenience sample based on clinics that use group billing numbers; it is unknown what proportion of all direct-to-consumer telemedicine is covered by this definition.

Of 5 229 240 individuals in our cohort (eFigure 1 in Supplement 1 ), 4 173 869 patients (79.8%) had their index virtual encounter with their own physician (mean [SD] age, 49.3 [21.5] years; 2 420 712 females [58.0%]) and 1 055 371 patients (20.2%) had their visit with an outside physician (mean [SD] age, 41.8[20.9] years; 605 614 females [57.4%]). Before matching, patients who had an outside-physician virtual encounter were younger, more often lived in a large urban area (859 772 patients [81.5%] vs 3 207 285 patients [76.8%]; SMD, 0.11), made fewer visits to their own physician in the prior 2 years (mean [SD] 3.2 [4.8] visits vs 7.0 [7.0] visits; SMD, 0.64), were less often enrolled to a non–team capitation model (331 770 patients [31.4%] vs 1 457 804 patients [34.9%]; SMD, 0.13), and more often had visits on weekends (107 945 patients [10.2%] vs 156 859 patients [3.8%]; SMD, 0.26) and using video (14 251 patients [1.4%] vs 9792 patients [0.2%]; SMD 0.13) compared with patients who had an own physician encounter (eTable 4 in Supplement 1 ).

After matching, there were no differences in measured characteristics of 942 983 matched pairs (total 1 885 966 patients) exceeding 10% (SMD of 0.1) ( Table 1 ; eTable 5 and eFigure 2 in Supplement 1 ). For example, the mean (SD) age was 42.3 (21.1) years in the outside physician group and 42.5 (20.8) years in the own physician group (SMD, 0.01). Patients who had a virtual visit with an outside physician saw that physician a median (IQR) of 0 (0-2) times in the 2 years prior to the index visit compared with 2 (0-5) visits with their own physician.

Patients who had a virtual encounter with an outside physician were 66% more likely to visit an emergency department within 7 days (30 748 patients [3.3%] vs 18 519 patients [2.0%]; RD, 1.3% [95% CI, 1.2%-1.3%]; RR, 1.66 [95% CI, 1.63-1.69]) ( Table 2 ). This corresponds to 1 additional emergency department visit for every 77 outside virtual visits. The increase in risk associated with virtual visits with outside vs own physicians was greater for the outcome of low-acuity (7759 patients [0.8%] vs 4084 patients [0.4%]; RR, 1.90 [95% CI, 1.83-1.97]) than high-acuity (7042 patients [0.7%] vs 4836 patients [0.5%]; RR, 1.46 [95% CI, 1.40-1.51]) emergency department visits within 7 days.

The increased risk of an emergency department visit was front-loaded, with an RR of 1.99 (95% CI, 1.93-2.05) on day 1 and 1.86 (95% CI, 1.78-1.93) on day 2 and early separation of survival curves (eFigure 3 in Supplement 1 ). However, there was still an association at 30 days (57 674 patients [6.1%] vs 41 342 patients [4.4%]; RR, 1.40 [95% CI, 1.38-1.41]).

Patients who had an outside-physician virtual encounter were more likely than those with an own physician visit to have an in-person family physician visit within 7 days (57 208 patients [6.1%] vs 45 828 patients [4.9%]; RR, 1.25 [95% CI, 1.23-1.26]) but were less likely to have such a visit with their own physician (9915 patients [1.1%] vs 39 102 patients [4.2%]; RR, 0.25 [95% CI, 0.25-0.26]). Similarly, they were nearly twice as likely to have a virtual visit within 7 days (83 681 patients [8.9%] vs 44 470 patients [4.7%]; RR, 1.88 [95% CI, 1.86-1.90]), and this was also less likely to be with their own physician (19 658 patients [2.1%] vs 39 251 patients [4.2%]; RR, 0.50 [95% CI, 0.49-0.51]).

The increase in risk of a 7-day emergency department visit associated with having an outside-physician virtual visit was greater for younger age groups. Children and adolescents (ages <18 years) were at highest risk (RR, 1.96 [95% CI, 1.86-2.05]), followed by adults (ages 18-64 years; RR, 1.69 [95% CI, 1.65-1.73]) and older adults (ages ≥65 years; RR, 1.40 [95% CI, 1.34-1.45]) (eTable 6 in Supplement 1 ). There were no differences between patients by residence in large urban, small urban, or rural areas.

The type of virtual visit was also associated with our tracer outcome of emergency department visits for high-acuity motor vehicle accidents, with a higher risk for those who visited an outside physician (129 people [0.01%]) than those who visited their own physician (97 people [0.01%]; RD, 0%; RR, 1.33 [95% CI, 1.02-1.73]). This association was weaker than the confounder strength of association ( e value 41 lower confidence limit = 2.32) that would be needed to explain away the findings for our primary outcome.

We used an alternative exposure definition (eTable 7 in Supplement 1 ) that compared a visit with a patient’s own physician with a visit with a known direct-to-consumer telemedicine clinic. In this analysis, 30 216 patients with a direct-to-consumer telemedicine visit were nearly 3 times more likely to visit the emergency department within 7 days than 30 216 patients with own physician visits (1878 patients [6.2%] vs 628 patients [2.1%]; RD, 4.1% [95% CI, 3.8%-4.5%]; RR, 2.99 [95% CI, 2.74-3.27]). Similar to findings in the main cohort, the increased risk was front-loaded in the first 2 days (eTable 8 and eFigure 4 in Supplement 1 ). Direct-to-consumer telemedicine users were more likely to have a repeat virtual visit within 7 days than those with a virtual visit to their own physician (3191 patients [10.6%] vs 1105 patients [3.7%]; RR, 2.89 [95% CI, 2.70-3.09]), although this was not explained by visits with the same physician, their own physician, or another physician in their group.

In this population-based cohort study, we compared outcomes of patients who received a virtual visit with their own family physician with outcomes of those who received a virtual visit with a family physician outside their physician group. We found that the latter patients had more visits to the emergency department in the ensuing 7 days, although the absolute difference was small, at 1.3%, corresponding to 1 additional emergency department visit for every 77 visits outside the group. We found that patients who had a visit with a subset of direct-to-consumer telemedicine clinics had approximately 3 times the risk of 7-day emergency department visits and repeat virtual visits than those who had a virtual visit with their own physician.

The increased use of the emergency department associated with low-continuity virtual visits was front-loaded in the first few days, suggesting that virtual visits may serve a triaging function, allowing for the identification of patients who would benefit from an in-person assessment. Direct-to-consumer telemedicine physicians may direct patients to an in-person follow-up visit, either immediately or if symptoms persist; alternatively, physicians may direct patients to the emergency department for a condition perceived to be urgent. Finally, some patients may choose the emergency department even if not suggested by a physician if they perceive this to be their only option for a timely in-person exam.

Patients having virtual visits outside the enrolling group were also more likely to have repeat virtual or in-person family physician visits, although these were less commonly with their enrolling physician, suggesting that these visits may have been repeat visits to walk-in clinics or virtual walk-in clinics. These patients may have faced challenges or barriers to accessing their usual physician or group, thus leading them to seek care elsewhere, including through a virtual walk-in clinic or an emergency department. Delays to an appointment, even for individuals already attached to a family physician, have been well described in the US and Canada. 43 , 44 However, visits with multiple outside clinicians may be associated with increased costs and fragmented care. 45 Care fragmentation increases the risk for errors and can introduce inconsistent and sometimes conflicting messages, eroding trust in physicians and the health care system as a whole. 18 , 46 - 48 Achieving timely access while preserving care continuity remains an important health policy challenge.

Our study is novel given that we directly contrasted outcomes after high- and low-continuity forms of virtual care. Meanwhile, previous research compared virtual visits with in-person visits. Within-system virtual care (integrated with in-person, office-based care) from US-based Intermountain Healthcare and Kaiser Permanente has compared more favorably with in-person care 49 , 50 than has direct-to-consumer telemedicine, which has been associated with increased downstream health care use and costs. 14 , 15 , 17 , 51 In 2 Ontario studies, 52 , 53 patients whose regular family physicians provided more virtual care did not have higher rates of emergency department use. Furthermore, within-system virtual care has demonstrated the potential for associated increases in equity of care 54 via improved access for individuals who struggle to afford higher visit-related costs. 55 - 57

Some health systems in the US and Europe have adopted integrated virtual care models. 49 , 54 , 58 However, other insurers, such as US Medicare, the UK National Health Service (NHS), and the Canadian provinces of Nova Scotia and British Columbia, have contracted out publicly insured health care services to corporate virtual clinics. 59 - 62 In a time series–based evaluation of privately insured residents of Minnesota, 12 plan members with coverage for direct-to-consumer telemedicine had lower costs of care for episodes of urinary tract infection but not sinusitis after plan expansion. An evaluation 63 of the NHS’s GP at Hand, a program of direct-to-consumer telemedicine supported by in-person locations, found that users had higher rates of consultations than the general population, despite being younger and in better health. This raised concerns about inequities given that public funding was diverted to the service. Policymakers continue to refine virtual care policies to optimize the balance of access, quality, and costs.

Our findings provide evidence to support policy changes that prioritize virtual visits within an existing therapeutic relationship. These findings complement those of McGrail et al 64 from a smaller study of patients who received virtual visits in British Columbia, Canada, in 2013 to 2014. In that study, potential cost savings from virtual visits were greater among patients who saw a known physician. We also build on a previous Ontario-based study 13 in which we found that patients who had visits with virtual walk-in clinics in 2020 were twice as likely as those with other types of virtual family physician visits to visit the emergency department within 30 days. In addition to better addressing potential sources of confounding, this study extends our findings to 2021, when emergency department visit volumes began to rise to their pre-COVID-19 levels. 52

Our study has several limitations. First, some outside-physician virtual visits may have averted emergency department visits for patients who were not able to access their own physician in a timely way. There is no way to identify such patients or determine which elements of access (eg, hours of operation, availability of physicians, or challenges making appointments) may have contributed. Second, we were limited to studying publicly funded virtual visits given that information on privately funded visits was not available. Third, the association between the type of virtual visit and trauma-related emergency department visits is unlikely to be explained by health care–seeking behavior alone given that we included only the highest-acuity visits. This association could reflect differences between groups in risk-taking behavior and health literacy. 65 However, confounding on this basis would not be strong enough to explain the association between the type of virtual visit and 7-day emergency department visits. Patients who perceive their care needs to be urgent or who have a lower tolerance for delays may be more likely to seek immediate care through direct-to-consumer telemedicine and emergency departments, another potential source of confounding. Fourth, Ontario residents do not pay premiums or copayments for any visit type included in this study. Therefore, our findings may not be fully generalizable to settings where outside group care is financially disincentivized.

This cohort study found that for patients attached to a family physician, a virtual visit with an outside physician instead of the patient’s own physician was associated with more emergency department visits and family physician visits in the following week. Our findings support the use of primary care virtual visits within an existing clinician-patient relationship.

Accepted for Publication: November 13, 2023.

Published: December 27, 2023. doi:10.1001/jamanetworkopen.2023.49452

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Lapointe-Shaw L et al. JAMA Network Open .

Corresponding Author: Lauren Lapointe-Shaw, MD, PhD, University Health Network, 200 Elizabeth St, Toronto, ON M5G 2C4, Canada ( [email protected] ).

Author Contributions: Dr Lapointe-Shaw had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Lapointe-Shaw, Bird, Hedden, Ivers, Martin, Shuldiner, Spithoff, Kiran.

Acquisition, analysis, or interpretation of data: Lapointe-Shaw, Salahub, Austin, Bai, Bhatia, Bird, Glazier, Hedden, Ivers, Tadrous, Kiran.

Drafting of the manuscript: Lapointe-Shaw, Salahub, Bird, Ivers.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Lapointe-Shaw, Bai.

Obtained funding: Lapointe-Shaw, Kiran.

Administrative, technical, or material support: Bhatia, Bird, Shuldiner.

Supervision: Lapointe-Shaw.

Conflict of Interest Disclosures: Dr Glazier reported receiving scientist support from MAP Centre for Urban Health Solutions and ICES and leadership stipends from the Canadian Institutes of Health Research outside the submitted work. No other disclosures were reported.

Funding/Support: This study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long-Term Care. This study also received funding from the MOH through an Innovations Strengthening Primary Health Care Through Research (INSPIRE-PHC) Applied Health Research Question grant awarded to Dr Lapointe-Shaw and project grant 175285 from the Canadian Institutes of Health Research awarded to Drs Lapointe-Shaw and Ivers.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The analyses, conclusions, opinions, and statements expressed herein are solely those of the authors and do not reflect those of the funding or data sources; no endorsement is intended or should be inferred.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: We would like to thank Alexander Kopp, BA, (ICES), for contributions to study design and data analysis and our patient partners Krysta Nesbitt and Patrick Roncal for their contributions to result interpretation. Alexander Kopp was not compensated for this work. Patient partners were provided with an hourly honorarium for reviewing study materials and attending project meetings.

Additional Information: This document used data adapted from the Statistics Canada Postal Code OM Conversion File, which is based on data licensed from the Canada Post Corporation and/or data adapted from the Ontario Ministry of Health (MOH) Postal Code Conversion File, which contains data copied under license from the Canada Post Corporation and Statistics Canada. Parts of this material are based on data and/or information compiled and provided by the MOH, Canadian Institutes of Health Research and Ontario Health.

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Cause of heart failure may differ for women and men

Mouse study identifies sex differences at the cellular level for heart failure with preserved ejection fraction (hfpef).

A new study from the UC Davis School of Medicine found striking differences at the cellular level between male and female mice with heart failure with preserved ejection fraction (HFpEF).

The findings could determine how HFpEF is treated in women compared to men.

With HFpEF, the heart muscle contracts normally but the heart is unable to fully relax and refill properly between beats. This condition is known as diastolic dysfunction. It can occur if the heart is too stiff or if the contraction process doesn't shut off quickly enough between beats.

The study showed that the diastolic dysfunction in female mice resulted from altered heart filament proteins. In male mice, it resulted from the slow removal of calcium from heart cells between heartbeats, causing a slight contraction to remain between beats.

The findings were published in Cardiovascular Research .

"This study demonstrates the importance of conducting research on both male and female populations," said Donald M. Bers, a senior author of the study. Bers is the chair of the Department of Pharmacology and the Joseph Silva Endowed Chair for Cardiovascular Research at the UC Davis School of Medicine. "If these same molecular male-female distinctions occur in obese diabetic patients with HFpEF, it may mean that the best therapeutic strategies for HFpEF in women may differ from those for men."

Heart failure is when the heart cannot pump enough blood and oxygen to support the body. Approximately 6.2 million people in the U.S. have heart failure. The five-year mortality rate for heart failure is around 50%, although many factors can influence survival. About half of those with heart failure have HFpEF, and almost twice as many women have HFpEF compared to men. Men with the heart failure may be more at risk of cardiac arrhythmias and sudden cardiac death.

"Two hit" mouse model to study HFpEF

Obesity and diabetes are common in people with HFpEF. To study the disease, the researchers created a unique "two-hit" mouse model combining two factors.

For the first factor, the researchers used mice genetically lacking a leptin receptor. Leptin is a hormone that promotes satiety. Without it, appetite remains high and the animals become obese and diabetic. For the second factor, mice were exposed to an aldosterone infusion. Aldosterone is a hormone made by the adrenal gland. High levels of aldosterone cause fluid retention.

This animal model of heart failure and diabetes develops HFpEF, allowing researchers to analyze the cellular and molecular mechanisms of muscle contraction and relaxation in male and female mice.

The research team (left to right): Christopher Y. Ko, Juliana Mira Hernandez, Donald M. Bers, Erin Y. Shen and Bence Hegyi in in front of their key findings on the screen.

Dysregulation of calcium, titin

Calcium is critical in the activation of contraction and relaxation of heart muscle cells as well as the heart's electrical activity. Calcium entering the heart cell at each beat causes the muscle to contract. It also helps drive the electric signal that synchronizes the contraction of the millions of heart muscle cells required for the heart to function as an efficient pump. Calcium is removed from the cell at each beat. This allows the heart to relax between beats and fill for the next beat.

In the male mice with HFpEF, the calcium removal from the heart muscle cells was slowed, preventing complete relaxation between beats. The male HFpEF mice also exhibited more abnormal heart rhythms, known as arrhythmias.

In contrast, the females with HFpEF exhibited normal calcium movements into and out of the heart cells. Instead, the researchers observed an increase in a shorter and stiffer form of titin (N2B). Titin is a protein in the heart that acts like a supportive spring. Researchers also observed phosphorylation (a molecular reaction) of titin and another heart filament protein, troponin I. Both the titin and troponin changes made the female heart cells functionally stiffer -- making the heart harder to fill -- even though calcium removal was normal.

"This study reveals different drug targets in males and females and will be a stepping-stone for future trials with sex-specific targeted drugs in HFpEF," said Bence Hegyi, an associate project scientist in the Bers Lab and co-senior author of the study. "Potentially, women with this form of HFpEF could benefit from drugs that reduce cardiac stiffness. On the other hand, men with this form of HFpEF might benefit more from drugs that enhance calcium removal."

Limitations

The researchers noted several limitations of the study. Although the mice in this study may be representative of the substantial number of HFpEF patients who have diabetes and are quite obese, many HFpEF patients may not be represented by this model. Multiple animal models will be needed to understand different subpopulations with HFpEF. Additional preclinical and clinical studies are needed to fully realize the potential benefits of this work.

Additional authors include Erin Shen, Christopher Ko, Emily Spencer, Daria Smoliarchuk and Julie Bossuyt from the UC Davis School of Medicine; Juliana Mira Hernandez from the UC Davis School of Medicine and the University of Antioquia, Medellin, Colombia; and Zaynab Hourani and Henk Granzier from the University of Arizona, Tucson.

• Heart Disease
• Stroke Prevention
• Cholesterol
• Diseases and Conditions
• Women's Health
• Prostate Cancer
• Triglycerides
• Testosterone
• Heart failure
• House mouse
• Biochemistry
• Encephalopathy

Story Source:

Materials provided by University of California - Davis Health . Original written by Lisa Howard. Note: Content may be edited for style and length.

Journal Reference :

• Juliana Mira Hernandez, Erin Y Shen, Christopher Y Ko, Zaynab Hourani, Emily R Spencer, Daria Smoliarchuk, Julie Bossuyt, Henk Granzier, Donald M Bers, Bence Hegyi. Differential sex-dependent susceptibility to diastolic dysfunction and arrhythmia in cardiomyocytes from obese diabetic heart failure with preserved ejection fraction model . Cardiovascular Research , 2024; DOI: 10.1093/cvr/cvae070

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