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Descriptive Research Design – Types, Methods and Examples

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Descriptive Research Design

Definition:

Descriptive research design is a type of research methodology that aims to describe or document the characteristics, behaviors, attitudes, opinions, or perceptions of a group or population being studied.

Descriptive research design does not attempt to establish cause-and-effect relationships between variables or make predictions about future outcomes. Instead, it focuses on providing a detailed and accurate representation of the data collected, which can be useful for generating hypotheses, exploring trends, and identifying patterns in the data.

Types of Descriptive Research Design

Types of Descriptive Research Design are as follows:

Cross-sectional Study

This involves collecting data at a single point in time from a sample or population to describe their characteristics or behaviors. For example, a researcher may conduct a cross-sectional study to investigate the prevalence of certain health conditions among a population, or to describe the attitudes and beliefs of a particular group.

Longitudinal Study

This involves collecting data over an extended period of time, often through repeated observations or surveys of the same group or population. Longitudinal studies can be used to track changes in attitudes, behaviors, or outcomes over time, or to investigate the effects of interventions or treatments.

This involves an in-depth examination of a single individual, group, or situation to gain a detailed understanding of its characteristics or dynamics. Case studies are often used in psychology, sociology, and business to explore complex phenomena or to generate hypotheses for further research.

Survey Research

This involves collecting data from a sample or population through standardized questionnaires or interviews. Surveys can be used to describe attitudes, opinions, behaviors, or demographic characteristics of a group, and can be conducted in person, by phone, or online.

Observational Research

This involves observing and documenting the behavior or interactions of individuals or groups in a natural or controlled setting. Observational studies can be used to describe social, cultural, or environmental phenomena, or to investigate the effects of interventions or treatments.

Correlational Research

This involves examining the relationships between two or more variables to describe their patterns or associations. Correlational studies can be used to identify potential causal relationships or to explore the strength and direction of relationships between variables.

Data Analysis Methods

Descriptive research design data analysis methods depend on the type of data collected and the research question being addressed. Here are some common methods of data analysis for descriptive research:

Descriptive Statistics

This method involves analyzing data to summarize and describe the key features of a sample or population. Descriptive statistics can include measures of central tendency (e.g., mean, median, mode) and measures of variability (e.g., range, standard deviation).

Cross-tabulation

This method involves analyzing data by creating a table that shows the frequency of two or more variables together. Cross-tabulation can help identify patterns or relationships between variables.

Content Analysis

This method involves analyzing qualitative data (e.g., text, images, audio) to identify themes, patterns, or trends. Content analysis can be used to describe the characteristics of a sample or population, or to identify factors that influence attitudes or behaviors.

Qualitative Coding

This method involves analyzing qualitative data by assigning codes to segments of data based on their meaning or content. Qualitative coding can be used to identify common themes, patterns, or categories within the data.

Visualization

This method involves creating graphs or charts to represent data visually. Visualization can help identify patterns or relationships between variables and make it easier to communicate findings to others.

Comparative Analysis

This method involves comparing data across different groups or time periods to identify similarities and differences. Comparative analysis can help describe changes in attitudes or behaviors over time or differences between subgroups within a population.

Applications of Descriptive Research Design

Descriptive research design has numerous applications in various fields. Some of the common applications of descriptive research design are:

• Market research: Descriptive research design is widely used in market research to understand consumer preferences, behavior, and attitudes. This helps companies to develop new products and services, improve marketing strategies, and increase customer satisfaction.
• Health research: Descriptive research design is used in health research to describe the prevalence and distribution of a disease or health condition in a population. This helps healthcare providers to develop prevention and treatment strategies.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs. This helps educators to improve teaching methods and develop effective educational programs.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs. This helps researchers to understand social behavior and develop effective policies.
• Public opinion research: Descriptive research design is used in public opinion research to understand the opinions and attitudes of the general public on various issues. This helps policymakers to develop effective policies that are aligned with public opinion.
• Environmental research: Descriptive research design is used in environmental research to describe the environmental conditions of a particular region or ecosystem. This helps policymakers and environmentalists to develop effective conservation and preservation strategies.

Descriptive Research Design Examples

Here are some real-time examples of descriptive research designs:

• A restaurant chain wants to understand the demographics and attitudes of its customers. They conduct a survey asking customers about their age, gender, income, frequency of visits, favorite menu items, and overall satisfaction. The survey data is analyzed using descriptive statistics and cross-tabulation to describe the characteristics of their customer base.
• A medical researcher wants to describe the prevalence and risk factors of a particular disease in a population. They conduct a cross-sectional study in which they collect data from a sample of individuals using a standardized questionnaire. The data is analyzed using descriptive statistics and cross-tabulation to identify patterns in the prevalence and risk factors of the disease.
• An education researcher wants to describe the learning outcomes of students in a particular school district. They collect test scores from a representative sample of students in the district and use descriptive statistics to calculate the mean, median, and standard deviation of the scores. They also create visualizations such as histograms and box plots to show the distribution of scores.
• A marketing team wants to understand the attitudes and behaviors of consumers towards a new product. They conduct a series of focus groups and use qualitative coding to identify common themes and patterns in the data. They also create visualizations such as word clouds to show the most frequently mentioned topics.
• An environmental scientist wants to describe the biodiversity of a particular ecosystem. They conduct an observational study in which they collect data on the species and abundance of plants and animals in the ecosystem. The data is analyzed using descriptive statistics to describe the diversity and richness of the ecosystem.

How to Conduct Descriptive Research Design

To conduct a descriptive research design, you can follow these general steps:

• Define your research question: Clearly define the research question or problem that you want to address. Your research question should be specific and focused to guide your data collection and analysis.
• Choose your research method: Select the most appropriate research method for your research question. As discussed earlier, common research methods for descriptive research include surveys, case studies, observational studies, cross-sectional studies, and longitudinal studies.
• Design your study: Plan the details of your study, including the sampling strategy, data collection methods, and data analysis plan. Determine the sample size and sampling method, decide on the data collection tools (such as questionnaires, interviews, or observations), and outline your data analysis plan.
• Collect data: Collect data from your sample or population using the data collection tools you have chosen. Ensure that you follow ethical guidelines for research and obtain informed consent from participants.
• Analyze data: Use appropriate statistical or qualitative analysis methods to analyze your data. As discussed earlier, common data analysis methods for descriptive research include descriptive statistics, cross-tabulation, content analysis, qualitative coding, visualization, and comparative analysis.
• I nterpret results: Interpret your findings in light of your research question and objectives. Identify patterns, trends, and relationships in the data, and describe the characteristics of your sample or population.
• Draw conclusions and report results: Draw conclusions based on your analysis and interpretation of the data. Report your results in a clear and concise manner, using appropriate tables, graphs, or figures to present your findings. Ensure that your report follows accepted research standards and guidelines.

When to Use Descriptive Research Design

Descriptive research design is used in situations where the researcher wants to describe a population or phenomenon in detail. It is used to gather information about the current status or condition of a group or phenomenon without making any causal inferences. Descriptive research design is useful in the following situations:

• Exploratory research: Descriptive research design is often used in exploratory research to gain an initial understanding of a phenomenon or population.
• Identifying trends: Descriptive research design can be used to identify trends or patterns in a population, such as changes in consumer behavior or attitudes over time.
• Market research: Descriptive research design is commonly used in market research to understand consumer preferences, behavior, and attitudes.
• Health research: Descriptive research design is useful in health research to describe the prevalence and distribution of a disease or health condition in a population.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs.

Purpose of Descriptive Research Design

The main purpose of descriptive research design is to describe and measure the characteristics of a population or phenomenon in a systematic and objective manner. It involves collecting data that describe the current status or condition of the population or phenomenon of interest, without manipulating or altering any variables.

The purpose of descriptive research design can be summarized as follows:

• To provide an accurate description of a population or phenomenon: Descriptive research design aims to provide a comprehensive and accurate description of a population or phenomenon of interest. This can help researchers to develop a better understanding of the characteristics of the population or phenomenon.
• To identify trends and patterns: Descriptive research design can help researchers to identify trends and patterns in the data, such as changes in behavior or attitudes over time. This can be useful for making predictions and developing strategies.
• To generate hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• To establish a baseline: Descriptive research design can establish a baseline or starting point for future research. This can be useful for comparing data from different time periods or populations.

Characteristics of Descriptive Research Design

Descriptive research design has several key characteristics that distinguish it from other research designs. Some of the main characteristics of descriptive research design are:

• Objective : Descriptive research design is objective in nature, which means that it focuses on collecting factual and accurate data without any personal bias. The researcher aims to report the data objectively without any personal interpretation.
• Non-experimental: Descriptive research design is non-experimental, which means that the researcher does not manipulate any variables. The researcher simply observes and records the behavior or characteristics of the population or phenomenon of interest.
• Quantitative : Descriptive research design is quantitative in nature, which means that it involves collecting numerical data that can be analyzed using statistical techniques. This helps to provide a more precise and accurate description of the population or phenomenon.
• Cross-sectional: Descriptive research design is often cross-sectional, which means that the data is collected at a single point in time. This can be useful for understanding the current state of the population or phenomenon, but it may not provide information about changes over time.
• Large sample size: Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Systematic and structured: Descriptive research design involves a systematic and structured approach to data collection, which helps to ensure that the data is accurate and reliable. This involves using standardized procedures for data collection, such as surveys, questionnaires, or observation checklists.

Advantages of Descriptive Research Design

Descriptive research design has several advantages that make it a popular choice for researchers. Some of the main advantages of descriptive research design are:

• Provides an accurate description: Descriptive research design is focused on accurately describing the characteristics of a population or phenomenon. This can help researchers to develop a better understanding of the subject of interest.
• Easy to conduct: Descriptive research design is relatively easy to conduct and requires minimal resources compared to other research designs. It can be conducted quickly and efficiently, and data can be collected through surveys, questionnaires, or observations.
• Useful for generating hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• Large sample size : Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Can be used to monitor changes : Descriptive research design can be used to monitor changes over time in a population or phenomenon. This can be useful for identifying trends and patterns, and for making predictions about future behavior or attitudes.
• Can be used in a variety of fields : Descriptive research design can be used in a variety of fields, including social sciences, healthcare, business, and education.

Limitation of Descriptive Research Design

Descriptive research design also has some limitations that researchers should consider before using this design. Some of the main limitations of descriptive research design are:

• Cannot establish cause and effect: Descriptive research design cannot establish cause and effect relationships between variables. It only provides a description of the characteristics of the population or phenomenon of interest.
• Limited generalizability: The results of a descriptive study may not be generalizable to other populations or situations. This is because descriptive research design often involves a specific sample or situation, which may not be representative of the broader population.
• Potential for bias: Descriptive research design can be subject to bias, particularly if the researcher is not objective in their data collection or interpretation. This can lead to inaccurate or incomplete descriptions of the population or phenomenon of interest.
• Limited depth: Descriptive research design may provide a superficial description of the population or phenomenon of interest. It does not delve into the underlying causes or mechanisms behind the observed behavior or characteristics.
• Limited utility for theory development: Descriptive research design may not be useful for developing theories about the relationship between variables. It only provides a description of the variables themselves.
• Relies on self-report data: Descriptive research design often relies on self-report data, such as surveys or questionnaires. This type of data may be subject to biases, such as social desirability bias or recall bias.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Introduction, developmental research.

• NORMATIVE STUDIES
• QUALITATIVE RESEARCH
• DESCRIPTIVE SURVEYS
• CASE STUDIES
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• Supplementary Content

Descriptive research is designed to document the factors that describe characteristics, behaviors and conditions of individuals and groups. For example, researchers have used this approach to describe a sample of individuals with spinal cord injuries with respect to gender, age, and cause and severity of injury to see whether these properties were similar to those described in the past. 1 Descriptive studies have documented the biomechanical parameters of wheelchair propulsion, 2 and the clinical characteristics of stroke. 3 As our diagram of the continuum of research shows, descriptive and exploratory elements are commonly combined, depending on how the investigator conceptualizes the research question.

Descriptive studies document the nature of existing phenomena and describe how variables change over time. They will generally be structured around a set of guiding questions or research objectives to generate data or characterize a situation of interest. Often this information can be used as a basis for formulation of research hypotheses that can be tested using exploratory or experimental techniques. The descriptive data supply the foundation for classifying individuals, for identifying relevant variables, and for asking new research questions.

Descriptive studies may involve prospective or retrospective data collection, and may be designed using longitudinal or cross-sectional methods (see Chapter 13 ). Surveys and secondary analysis of clinical databases are often used as sources of data for descriptive analysis. Several types of research can be categorized as descriptive, including developmental research, normative research, qualitative research and case studies. The purpose of this chapter is to describe these approaches.

Concepts of human development, whether they are related to cognition, perceptual-motor control, communication, physiological change, or psychological processes, are important elements of a clinical knowledge base. Valid interpretation of clinical outcomes depends on our ability to develop a clear picture of those we treat, their characteristics and performance expectations under different conditions. Developmental research involves the description of developmental change and the sequencing of behaviors in people over time. Developmental studies have contributed to the theoretical foundations of clinical practice in many ways. For example, the classic descriptive studies of Gesell and Amatruda 4 and McGraw 5 provide the basis for much of the research on sequencing of motor development in infants and children. Erikson's studies of life span development have contributed to an understanding of psychological growth through old age. 6

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Methodology

• Descriptive Research | Definition, Types, Methods & Examples

Descriptive Research | Definition, Types, Methods & Examples

Published on May 15, 2019 by Shona McCombes . Revised on June 22, 2023.

Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what , where , when and how   questions , but not why questions.

A descriptive research design can use a wide variety of research methods  to investigate one or more variables . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

Table of contents

When to use a descriptive research design, descriptive research methods, other interesting articles.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when and where it happens.

Descriptive research question examples

• How has the Amsterdam housing market changed over the past 20 years?
• Do customers of company X prefer product X or product Y?
• What are the main genetic, behavioural and morphological differences between European wildcats and domestic cats?
• What are the most popular online news sources among under-18s?
• How prevalent is disease A in population B?

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Descriptive research is usually defined as a type of quantitative research , though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable .

Survey research allows you to gather large volumes of data that can be analyzed for frequencies, averages and patterns. Common uses of surveys include:

• Describing the demographics of a country or region
• Gauging public opinion on political and social topics
• Evaluating satisfaction with a company’s products or an organization’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event or organization). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalizable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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• Published: 04 November 2009

A descriptive study of medical educators' views of problem-based learning

• Mohsen Tavakol 1 ,
• Reg Dennick 1 &
• Sina Tavakol 2  

BMC Medical Education volume  9 , Article number:  66 ( 2009 ) Cite this article

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There is a growing amount of literature on the benefits and drawbacks of Problem-Based Learning (PBL) compared to conventional curricula. However, it seems that PBL research studies do not provide information rigorously and formally that can contribute to making evidence-based medical education decisions. The authors performed an investigation aimed at medical education scholars around the question, "What are the views of medical educators concerning the PBL approach?"

After framing the question, the method of data collection relied on asking medical educators to report their views on PBL. Two methods were used for collecting data: the questionnaire survey and an online discussion forum.

The descriptive analysis of the study showed that many participants value the PBL approach in the practice and training of doctors. However, some participants hold contrasting views upon the importance of the PBL approach in basic medical education. For example, more than a third of participants (38.5%) had a neutral stance on PBL as a student-oriented educational approach. The same proportion of participants also had a neutral view of the efficiency of traditional learning compared to a PBL tutorial. The open-ended question explored the importance of faculty development in PBL. A few participants had negative perceptions of the epistemological assumptions of PBL. Two themes emerged from the analysis of the forum repliers: the importance of the faculty role and self-managed education.

Whilst many participants valued the importance of the PBL approach in the practice and training of doctors and agreed with most of the conventional descriptions of PBL, some participants held contrasting views on the importance of the PBL approach in undergraduate medical education. However there was a strong view concerning the importance of facilitator training. More research is needed to understand the process of PBL better.

Peer Review reports

PBL is possibly one of the most innovative themes in medical education; it has raised extreme debate and still continues to generate passionate discussions. There is a growing amount of literature on the benefits and drawbacks of PBL compared to conventional curricula. The experimental studies reported in the three reviews published in 1993 [ 1 – 3 ] showed that there is a dearth of good quality studies and evidence available regarding the hypothesis that PBL produces learners different to or superior to those derived from traditional methods [ 4 ]. This has led supporters and detractors to continue to investigate further the epistemological and ontological issues arising from the processes and outcomes of PBL. However, it has been asserted that the quality of medical education research is poor, repetitive, not informed by theory, methodologically weak and does not pay attention to validity threats in quasi-experimental designs [ 5 , 6 ]. A critical reading of studies on the methods and findings of PBL showed that they had not provided an evidence-base indicating the educational superiority of PBL despite the fact that such studies underpinned the effectiveness of PBL on attitudes, perceptions, self-rating and opinions [ 7 ]. It has also been argued that all forms of research involving subjectivity such as ethnography, grounded theory and phenomenology have been "unscientific" due to a lack of explicability, repeatability and replicability [ 8 ]. Therefore, qualitative studies, which explored the experiences and perceptions of students and tutors in programs that incorporated student-centred problem-based pedagogy, may not provide the best available evidence for the effectiveness of PBL curricula. Similarly, quantitative studies which compared the PBL approach with conventional teaching, might not illustrate the potential impact that it can have, if statistical effect size measures are not reported [ 9 ].

With respect to learning theories, PBL arose from the personal experiences and beliefs of a few medical educators [ 10 ] and it was arguably non-theoretical in its development. However, as PBL has evolved, some learning theories were claimed to support PBL [ 11 ]. In medical education, PBL has its roots in constructivist theories of learning [ 12 ]. However, Colliver has asserted that constructivism is not a theory of learning. "It provides a fleeting insight into the learning process, but it is not a theory of learning. It confuses epistemology and learning, and it would seem to offer little of value to medical education" [ 13 ]. Furthermore, when appraising some PBL quantitative papers, we noticed that the studies were not based on any learning theory or were not testing predictions from a learning theory. If a study tests a prediction or hypothesis based on a theory and the findings are consistent with the theory, then the findings are considered to support that theory [ 14 ]. Learning theory has not been used to design quantitative PBL studies and data from studies has not been used to support theory. Perhaps corruptions of quantitative inquiry approaches in recent years place the credibility of PBL at stake, and it may be argued that the findings generated are trivial or obvious.

Taken together, these ideas seem to indicate that PBL research studies do not provide information rigorously and formally that contribute to making evidence-based medical education decisions. Perhaps for this reason medical education scholars are still uncertain whether the PBL approach creates better physicians compared with traditional learning, or whether the PBL approach is superior to didactic basic and clinical teaching. Is "the glass half-full"? [ 15 ] or just "half empty?" [ 16 ]. While the benefits of curriculum reform are strongly cited, especially the increased use of PBL, there is a dearth of research assessing the effects of various curricula including PBL on preclinical and clinical measures of student performance. The exception to this is the longitudinal study on the impact of various curricula (including PBL) on student learning once they begin clinical practice. The authors concluded that changing curricula in medical education reform is not likely to have an impact on improvements in student achievement [ 17 ]. We do agree with Wood who stated that "performing outcomes based research in education is difficult because of the large range of confounding factors" [ 18 ]. Contrary to the conclusion of Wood, it seems that, for PBL, we do need to continue "arguing about the process and examine outcomes". This may bolster the promise of replication studies, which are necessary for the formation of a body of best evidence-based medical education practice, particularly for PBL.

We felt it important, therefore, to conduct a study, which is grounded in the benefits and drawbacks of current PBL research findings. We asked ourselves: What are the views of medical educators concerning the PBL approach? This study provides a new picture that may add to our overall understanding of PBL.

The study started in March 2006 in the UK, with a planned recruitment period of 18 months. The method of data collection relied on asking medical educators to report their views in a survey. Ethical approval was not sought as this was an opportunistic sample from volunteers at a one-day conference and web-based survey and by opting to reply to the questionnaire [see additional file 1 ], the participants automatically agreed to take part in this study, and consequently a consent form was not presented to them. The survey was an anonymous study. Two methods were used for collecting data. Firstly, questionnaires were distributed to a convenience sample of 65 medical educators, who participated in the 3 rd UK conference on Graduate Entry Medicine (GEM), 14 th July 2006. The number of completed, useable questionnaires was 33, giving a response rate of 51%. This low response rate led us to collect questionnaire data through the Internet in order to increase the sample size. For this, we embedded the same questionnaire in a web application that was only accessible through a confidential hyperlink. After a list of potential respondents was created (n = 27), an email including the hyperlink was sent out to the members of that list inviting them to participate in this study. The use of follow up reminders was ineffective in achieving higher response rate for the web-based survey. Six medical educators filled in the web-based survey. Table 1 shows the characteristics of participants.

The second method for collecting data was a discussion forum entitled "What have we learned from the PBL approach?" An email was forwarded to the members of the Evidence Based Medical Education (EBME) collaboration in order to ascertain their view on PBL. We asked medical educators who have experienced PBL to discuss their views on the PBL approach. Six members commented regarding the above question in a forum discussion. Therefore, in this study the purposive sample consisted of 39 medical education scholars and 6 forum repliers, with firsthand experience of PBL.

The design of the questionnaire was based on a thorough review of the literature relating to PBL studies. The PBL scale consists of 17 items about the conditions that hinder and support PBL. To reduce the bias of the questionnaire, some items were written negatively, so that not all questions reflected positive views towards PBL. Each item was accompanied by a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). An open-ended question was provided to find out the medical educator's experience concerning the PBL approach. Medical educators also provided demographic information, which included items on their age, gender, and experience. A brief instruction for the completion of the study instrument was provided to ensure that it could be self-administered.

Prior to conducting the survey, the content validity of the instrument was established by subjecting it to review by two PBL experts. The experts were selected based on their deep experiences of PBL and their knowledge of the PBL process in their own school. We asked them to criticise the statements if they did not make sense or cover the purpose of the study. We took their comments on the questionnaire design into consideration, and we made appropriate modifications to clarifying meaning. We then tested the questionnaire for reliability with data from a group of individual participants (n = 20). The reliability of the tool was determined by computation of Chronbach's alpha using SPSS, which gave a value of 0.68, indicating an acceptable degree of internal consistency.

Because this study was primarily descriptive, descriptive information was presented for numerical data analysis. Words or sentences provided by participants in the open-ended questions have been reported in a table. The forum replies were also read and re-read in order to identify emerging themes as headings under which we can categorise most of the data.

The results of this study can best be treated under three headings: the PBL scale, open-ended question, and forum repliers.

The PBL scale

A sample of 39 medical educators from an accessible population was recruited (Table 1 ). The mean number of years work experience with facilitating was 7 years (SD 6.3, minimum 1 year, and maximum 30 years). Participants were asked to rate the extent to which they perceived each of 17 items. Responses to agree and strongly agree were combined as "agree" and to disagree and strongly disagree were combined as "disagree". Most (69.2%) of respondents agreed that there is a difference between a PBL course and a conventional course. When asked to report whether they experienced PBL as a student- centred approach, more than a third of the respondents (36.2%) agreed. In response to the item, 'the facilitator needs to be expert in the subject matter of the case', the majority of respondents (61.6%) disagreed. More than a half of the respondents (51.3%) disagreed that 'Learning from a large group lecture is a more efficient way of learning than a PBL tutorial. Some respondents (35.9%) felt 'neutral' about increasing the number of doctors in the UK using graduate entry PBL. Most educators (62%) disagreed that the facilitator is not redundant in a PBL tutorial meeting. More than one fourth of respondents (25.7%) agreed that students are forced to participate in PBL by the facilitator. Few respondents (15.4%) had a neutral view on this. When asked to report if a lecture-based environment makes for better job satisfaction compared with a PBL course, more than a half of the respondents (51.3%) disagreed. The majority of participants (51.2%) disagreed that the students on a PBL course spend too much time elaborating their knowledge in comparison with a conventional course. As Table 2 shows, many participants valued the importance of the PBL approach in the practice and training of doctors. However, some participants held contrasting views upon the importance of the PBL approach in undergraduate medical education. For example, the scores showed that most participants had a neutral view of the efficiency of lecture-based learning compared to a PBL tutorial.

The open-ended question

Respondents were asked in an open ended question for their opinions on lessons they have learned or experienced during PBL tutorials. Of note was the low response to this question. For this reason, we analysed words and terms provided by the participants (Table 3 ). It is apparent from this table that the participants had concerns about issues relating to facilitators (items 3, 5, 6). The findings also indicated the importance that participants placed on student learning in PBL. One participant had concerns about the use of the PBL approach for Graduate Entry Medicine.

The forum repliers

Two general themes emerged from the forum repliers concerning medical educators' experiences of the PBL approach. They are: faculty role and self-managed education. We will now look at each of these in turn.

Faculty role

Participants in the forum had different views with respect to the PBL approach. One participant, who had graduated in medicine and experienced PBL, reflected that the PBL approach was useful in teaching 3 rd year medical students who are just entering their clinical training. This is because students integrate basic science with clinical application. Although one of the principal ideas behind PBL is that students aim their learning at the areas in which their knowledge is more deficient, one participant asserted that students sometimes " do not know what they don't know ". This finding may show that students are 'unconsciously incompetent', on the first stage of the conscious-competence framework in PBL. The participant described the facilitator role as crucial to effective learning in the PBL tutorial. He continued that students who had a process expert in discussion failed to catch key concepts and key pieces of information in their literature searching, or key insights in terms of understanding the questions they are addressing. The situation described below exemplifies this behaviour:

"...on the one hand clear objectives and faculty development are necessary so students are properly advised through the PBL exercise, such that they take ownership of their own learning and true self-directed learning can happen. Especially early in medical training, students cannot know what they need to learn in order to solve the problem. On the other hand, affirmation from a tutor that students are on the right track can very easily turn into direction from the tutor, and that can turn into teaching by the tutor."

A consultant stressed the importance of the faculty role in the PBL approach and strategies for successful facilitation. He found that the main barrier to implementation of PBL is the lack of preparation of faculty members to facilitate self-directed learning.

Self-managed education

In terms of the student-centred nature of the PBL approach, with its emphasis on self-directed learning, one respondent stated that the learning objectives of a PBL course do not provide the opportunity to encourage students to take greater ownership of their work, and hence greater responsibility for their learning. A participant replied:

"If clear learning objectives are prepared (not a list of subjects or objectives that use ambiguous words such as 'to understand'), and a series of concepts or principles are identified (those that the faculty think can be missed by the students), then the students can become truly a self-directed learner exerting a high degree of autonomy."

Another participant reflected on this situation:

"For me, the key question is, to what degree we believe in self-directed learning and convey to the students the message that they can take responsibility for their learning without our intervention?"

These perceptions indicate that the self-directed nature of PBL is still challenging. This may show that the participants interpreted self-directed learning as surface oriented self teaching. As such, this may indicate the students do not have control over all elements of the PBL process. Students, for example, have no control over the scenario, although the nature of self-directed learning of PBL is acknowledged.

This study has methodological limitations that must be taken into consideration when interpreting the findings. One cannot over-emphasise the limitations of self-report as this may limit the validity of findings. Respondents for various reasons may under, or overestimate their practice. A methodological problem frequently associated with the use of self-reports in questionnaires, which may have been evident in the present study, is the inability to determine the extent to which responses accurately reflect the respondents' experiences and expectations of their PBL tutorial sessions. This warrants further research to examine the actual PBL process. It is also possible that medical educators in this study were not representative of PBL educators.

The response rate was low, despite our efforts to maximise it and this means that the findings should be interpreted with caution. Reasons for non-response are not known. Non-respondents to the survey may also be less interested or involved in PBL, and therefore the reported extent of the PBL approach in this study may be higher than in reality.

Regarding forum repliers, this was a convenience sample consisting of only 6 medical educators. The online forum discussions were convenient and provided a transcribed record. Drawbacks to participation in online discussions may be the same as for online education in general, that is, the inability to capture the richness and depth of meaning without visual and verbal clues.

To overcome these methodological limitations we suggest, therefore, randomised experiments which focus on the performance of PBL graduates and non-PBL graduates in the clinical workplace. This may optimise the accuracy of inferences about the PBL approach. Clearly, an important task facing researchers is the identification and control of those factors that may give rise to alternative explanations for the effects of PBL compared to non-PBL methods. Factors such as the educational background of the students, methods of student selection and the learning culture of the institution are all potentially important. In addition perhaps more emphasis should be placed on researching the comparative learning processes that PBL and non-PBL students engage in. For example PBL students engage in considerably more verbal discourse, questioning and reasoning episodes than traditional students. Perhaps this develops additional cognitive and interpersonal skills not necessarily acquired to the same extent by more didactic and teacher-centred learning methods.

The descriptive analysis of this study showed that many participants valued the PBL approach in the practice and training of doctors. However, some medical education scholars held contrasting views upon on the importance of the PBL approach in undergraduate medical education. Among the medical educators surveyed, 38.5% had a neutral experience of PBL as a student-oriented educational approach. This finding is not consistent with the common characteristic of the PBL approach, indicating its student-centred nature [ 19 ]. Although 46.2% of participants valued PBL as a student-oriented approach, the question that comes to mind is why do a group of medical educators feel so uncertain about it? Further research should examine this. What is surprising is that more than 61% of medical educators disagreed that the facilitator needs to be an expert in the subject matter of the case despite the fact that the majority of participants had a medical health professional qualification. The issue of content knowledge compared to process expertise is still challenging. Some evidence shows differences in favour of content experts when compared with process expertise [ 20 ]. For example, Eagle et al . concluded that twice as many learning issues were identified by groups led by content experts [ 21 ]. Consistent with the results of these studies, Schmidt et al concluded that students guided by subject experts spent more time on self-directed learning and achieved somewhat better scores on high stakes tests than students guided by non-expert facilitators [ 22 ]. However, a study by Silver and Wilkerson indicated that content expertise resulted in more tutor-directed discussion in a PBL course [ 23 ]. Taken together, these studies may suggest that both subject and process expertise are required by facilitators.

The results of this study indicate that the participants had a neutral view of the efficiency of traditional learning compared to a PBL tutorial. As such, participants had a neutral view of the claim that knowledge is better acquired in PBL-based course rather than a lecture-based one. These findings add to most previous research studies by demonstrating that there is no difference between the knowledge that PBL students and non-PBL students acquire about medical sciences [ 24 ]. Although studies show that group learning in PBL may have positive effects, much more empirical evidence is needed to obtain deeper insight into the productive group learning of a PBL tutorial [ 25 ]. One may argue that the process of PBL needs to be rigorously investigated in order to offer reasons for believing that it is designed to help student construct an extensive knowledge base and to become doctors dedicated to lifelong-learning. It is therefore important to further explore the nature of the learning acquired from PBL courses compared to traditional instruction courses.

With respect to graduate entry PBL, this study did not show that the policy of admitting graduates versus school-leavers to medical programmes was perceived as effective in creating better doctors. Interestingly, no previous PBL studies have explored differences between graduate entry PBL and school leaver programmes, although this study revealed that graduate entry PBL is not perceived as a more effective way of increasing the number of doctors in the UK by the majority of responders. In addition, this study revealed that there was a majority perception that graduate entry PBL will produce doctors who have come from a greater variety of educational backgrounds. However, will graduate entry PBL create better doctors compared to school leaver programmes? Sophisticated methodological approaches are required to answer this question.

The descriptions of medical educators about the PBL approach focused on the process of PBL, the characteristics of a good PBL facilitator and the advantages and disadvantages of PBL. It has been well documented that the facilitator role is central to PBL. The adoption of the role requires an understanding of epistemological and ontological issues about teaching and learning in medicine. In the epistemological sense PBL students are novices and the knowledge facilitator should assist them in restructuring new knowledge based on their prior declarative and procedural knowledge. In the ontological sense perceiving a new reality by students is important and the role of the facilitator is to assist students to explore reality in different ways. As the importance of faculty development in PBL was valued by participants in the forum discussion this may suggest more facilitator development workshops to help achieve competence as skilled facilitators of the PBL process. Such workshops may uncover conflicting roles of tutors in the steps of the PBL process. As Irby indicated, identifying and practicing these roles (mediator, challenger, negotiator, director, evaluator and listener) is a key skill of effective facilitation [ 26 ].

In addition to this, one medical educator had a negative approach about PBL, and reflected: " PBL is still unclear in GEM ". It seems that some medical educators have negative perceptions of the ontological assumptions of PBL. For instance, a qualitative study was conducted to explore how a cohort of tutors made sense of PBL. In this study, one participant stated: " absolutely not, no views not really changed at all. I'm still not convinced that PBL, despite the fact that [I will tutor again] is the proper way of teaching" [ 27 ]. Altogether these findings concerning academic achievement are slightly in favour of non-PBL programmes.

When asked about their experience in a PBL tutorial course, medical educators indicated they had few negative feelings with respect to facilitating self-directed learning and student learning. There are several possible reasons for this. Firstly, in the beginning of the course, it seems that the students find adopting a self-directed problem-based approach to learning difficult as they "do not know what they do not know". This may be attributed to the fact that students may have a restricted personal knowledge of the complexity of the "case". Secondly, students may not have clear objectives for the behaviour that they have to achieve, particularly in clinical settings, as mentioned by one participant. Thirdly, learning styles, both deep, surface and 'strategic', are determined at secondary school, and it is also difficult to influence learning styles even with a PBL curriculum [ 28 , 29 ].

In this study, a few participants suggested combinations of pedagogical strategies, where several PBL courses are offered along with courses presented in a more traditional way. There is no evidence that indicates how a hybrid curriculum can make students better doctors compared to other approaches. However, a recent study concluded that changing curricula in medical education reform is not likely to have an impact in improvement in student achievement [ 17 ]. The authors suggested that further work ought to focus on student characteristics and teacher characteristics such as teaching competency.

Whilst many participants valued the importance of the PBL approach in the practice and training of doctors and agreed with most of the conventional descriptions of PBL, some participants held contrasting views upon the importance of the PBL approach in undergraduate medical education. For example, most participants had a neutral view of the efficiency of lecture-based learning compared to a PBL tutorial. However there was a strong view concerning the importance of facilitator training. We need to understand the process of PBL better.

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The authors would like to express their gratitude to all the medical educators who participated in this study. Thanks also to the two reviewers whose comments allowed us to improve on our previous draft.

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Tavakol, M., Dennick, R. & Tavakol, S. A descriptive study of medical educators' views of problem-based learning. BMC Med Educ 9 , 66 (2009). https://doi.org/10.1186/1472-6920-9-66

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For Julie Greenberg, a career of research, mentoring, and advocacy

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For Julie E. Greenberg SM ’89, PhD ’94, what began with a middle-of-the-night phone call from overseas became a gratifying career of study, research, mentoring, advocacy, and guiding of the office of a unique program with a mission to educate the next generation of clinician-scientists and engineers.

In 1987, Greenberg was a computer engineering graduate of the University of Michigan, living in Tel Aviv, Israel, where she was working for Motorola — when she answered an early-morning call from Roger Mark , then the director of the Harvard-MIT Program in Health Sciences and Technology (HST). A native of Detroit, Michigan, Greenberg had just been accepted into MIT’s electrical engineering and computer science (EECS) graduate program.

HST — one of the world’s oldest interdisciplinary educational programs based on translational medical science and engineering — had been offering the medical engineering and medical physics (MEMP) PhD program since 1978, but it was then still relatively unknown. Mark, an MIT distinguished professor of health sciences and technology and of EECS, and assistant professor of medicine at Harvard Medical School, was calling to ask Greenberg if she might be interested in enrolling in HST’s MEMP program.

“At the time, I had applied to MIT not knowing that HST existed,” Greenberg recalls. “So, I was groggily answering the phone in the middle of the night and trying to be quiet, because my roommate was a co-worker at Motorola, and no one yet knew that I was planning to leave to go to grad school. Roger asked if I’d like to be considered for HST, but he also suggested that I could come to EECS in the fall, learn more about HST, and then apply the following year. That was the option I chose.”

For Greenberg, who retired March 15 from her role as senior lecturer and director of education — that early morning phone call was the first she would hear of the program where she would eventually spend the bulk of her 37-year career at MIT, first as a student, then as the director of HST’s academic office. During her first year as a graduate student, she enrolled in class HST.582/6.555 (Biomedical Signal and Image Processing), for which she later served as lecturer and eventually course director, teaching the class almost every year for three decades. But as a first-year graduate student, she says she found that “all the cool kids” were HST students. “It was a small class, so we all got to know each other,” Greenberg remembers. “EECS was a big program. The MEMP students were a tight, close-knit community, so in addition to my desire to work on biomedical applications, that made HST very appealing.”

Also piquing her interest in HST was meeting Martha L. Gray, the Whitaker Professor in Biomedical Engineering. Gray, who is also a professor of EECS and a core faculty member of the MIT Institute for Medical Engineering and Science (IMES), was then a new member of the EECS faculty, and Greenberg met her at an orientation event for graduate student women, who were a smaller cohort then, compared to now. Gray SM ’81, PhD ’86 became Greenberg’s academic advisor when she joined HST. Greenberg’s SM and PhD research was on signal processing for hearing aids, in what was then the Sensory Communication Group in MIT’s Research Laboratory of Electronics (RLE).

Gray later succeeded Mark as director of HST at MIT, and it was she who recruited Greenberg to join as HST director of education in 2004, after Greenberg had spent a decade as a researcher in RLE.

“Julie is amazing — one of my best decisions as HST director was to hire Julie. She is an exceptionally clear thinker, a superb collaborator, and wicked smart,” Gray says. “One of her superpowers is being able to take something that is incredibly complex and to break it down into logical chunks … And she is absolutely devoted to advocating for the students. She is no pushover, but she has a way of coming up with solutions to what look like unfixable problems, before they become even bigger.”

Greenberg’s experience as an HST graduate student herself has informed her leadership, giving her a unique perspective on the challenges for those who are studying and researching in a demanding program that flows between two powerful institutions. HST students have full access to classes and all academic and other opportunities at both MIT and Harvard University, while having a primary institution for administrative purposes, and ultimately to award their degree. HST’s home at Harvard is in the London Society at Harvard Medical School, while at MIT, it is IMES.

In looking back on her career in HST, Greenberg says the overarching theme is one of “doing everything possible to smooth the path. So that students can get to where they need to go, and learn what they need to learn, and do what they need to do, rather than getting caught up in the bureaucratic obstacles of maneuvering between institutions. Having been through it myself gives me a good sense of how to empower the students.”

Rachel Frances Bellisle, an HST MEMP student who is graduating in May and is studying bioastronautics, says that having Julie as her academic advisor was invaluable because of her eagerness to solve the thorniest of issues. “Whenever I was trying to navigate something and was having trouble finding a solution, Julie was someone I could always turn to,” she says. “I know many graduate students in other programs who haven’t had the important benefit of that sort of individualized support. She’s always had my back.”

And Xining Gao, a fourth-year MEMP student studying biological engineering, says that as a student who started during the Covid pandemic, having someone like Greenberg and the others in the HST academic office — who worked to overcome the challenges of interacting mostly over Zoom — made a crucial difference. “A lot of us who joined in 2020 felt pretty disconnected,” Gao says. “Julie being our touchstone and guide in the absence of face-to-face interactions was so key.” The pandemic challenges inspired Gao to take on student government positions, including as PhD co-chair of the HST Joint Council. “Working with Julie, I’ve seen firsthand how committed she is to our department,” Gao says. “She is truly a cornerstone of the HST community.”

During her time at MIT, Greenberg has been involved in many Institute-level initiatives, including as a member of the 2016 class of the Leader to Leader program. She lauded L2L as being “transformative” to her professional development, saying that there have been “countless occasions where I’ve been able to solve a problem quickly and efficiently by reaching out to a fellow L2L alum in another part of the Institute.”

Since Greenberg started leading HST operations, the program has steadily evolved. When Greenberg was a student, the MEMP class was relatively small, admitting 10 students annually, with roughly 30 percent of them being women. Now, approximately 20 new MEMP PhD students and 30 new MD or MD-PhD students join the HST community each year, and half of them are women. Since 2004, the average time-to-degree for HST MEMP PhD students dropped by almost a full year, and is now on par with the average for all graduate programs in MIT’s School of Engineering, despite the complications of taking classes at both Harvard and MIT. 

A search is underway for Julie’s replacement. But in the meantime, those who have worked with her praise her impact on HST, and on MIT.

“Throughout the entire history of the HST ecosystem, you cannot find anyone who cares more about HST students than Julie,” says Collin Stultz, the Nina T. and Robert H. Rubin Professor in Medical Engineering and Science, and professor of EECS. Stultz is also the co-director of HST, as well as a 1997 HST MD graduate. “She is, and has always been, a formidable advocate for HST students and an oracle of information to me.”

Elazer Edelman ’78, SM ’79, PhD ’84, the Edward J. Poitras Professor in Medical Engineering and Science and director of IMES, says that Greenberg “has been a mentor to generations of students and leaders — she is a force of nature whose passion for learning and teaching is matched by love for our people and the spirit of our institutions. Her name is synonymous with many of our most innovative educational initiatives; indeed, she has touched every aspect of HST and IMES this very many decades. It is hard to imagine academic life here without her guiding hand.”

Greenberg says she is looking forward to spending more time on her hobbies, including baking, gardening, and travel, and that she may investigate getting involved in some way with working with STEM and underserved communities. She describes leaving now as “bittersweet. But I think that HST is in a strong, secure position, and I’m excited to see what will happen next, but from further away … and as long as they keep inviting alumni to the HST dinners, I will come.”

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This paper is in the following e-collection/theme issue:

Published on 3.4.2024 in Vol 26 (2024)

Public Discourse, User Reactions, and Conspiracy Theories on the X Platform About HIV Vaccines: Data Mining and Content Analysis

Authors of this article:

Original Paper

• Jueman M Zhang 1 , PhD   ; 
• Yi Wang 2 , PhD   ; 
• Magali Mouton 3   ; 
• Jixuan Zhang 4   ; 
• Molu Shi 5 , PhD  

1 Harrington School of Communication and Media, University of Rhode Island, Kingston, RI, United States

2 Department of Communication, University of Louisville, Louisville, KY, United States

3 School of Rehabilitation Sciences, University of Ottawa, Ottawa, ON, Canada

4 Polk School of Communications, Long Island University, Brooklyn, NY, United States

5 College of Business, University of Louisville, Louisville, KY, United States

Corresponding Author:

Jueman M Zhang, PhD

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Background: The initiation of clinical trials for messenger RNA (mRNA) HIV vaccines in early 2022 revived public discussion on HIV vaccines after 3 decades of unsuccessful research. These trials followed the success of mRNA technology in COVID-19 vaccines but unfolded amid intense vaccine debates during the COVID-19 pandemic. It is crucial to gain insights into public discourse and reactions about potential new vaccines, and social media platforms such as X (formerly known as Twitter) provide important channels.

Objective: Drawing from infodemiology and infoveillance research, this study investigated the patterns of public discourse and message-level drivers of user reactions on X regarding HIV vaccines by analyzing posts using machine learning algorithms. We examined how users used different post types to contribute to topics and valence and how these topics and valence influenced like and repost counts. In addition, the study identified salient aspects of HIV vaccines related to COVID-19 and prominent anti–HIV vaccine conspiracy theories through manual coding.

Methods: We collected 36,424 English-language original posts about HIV vaccines on the X platform from January 1, 2022, to December 31, 2022. We used topic modeling and sentiment analysis to uncover latent topics and valence, which were subsequently analyzed across post types in cross-tabulation analyses and integrated into linear regression models to predict user reactions, specifically likes and reposts. Furthermore, we manually coded the 1000 most engaged posts about HIV and COVID-19 to uncover salient aspects of HIV vaccines related to COVID-19 and the 1000 most engaged negative posts to identify prominent anti–HIV vaccine conspiracy theories.

Results: Topic modeling revealed 3 topics: HIV and COVID-19, mRNA HIV vaccine trials, and HIV vaccine and immunity. HIV and COVID-19 underscored the connections between HIV vaccines and COVID-19 vaccines, as evidenced by subtopics about their reciprocal impact on development and various comparisons. The overall valence of the posts was marginally positive. Compared to self-composed posts initiating new conversations, there was a higher proportion of HIV and COVID-19–related and negative posts among quote posts and replies, which contribute to existing conversations. The topic of mRNA HIV vaccine trials, most evident in self-composed posts, increased repost counts. Positive valence increased like and repost counts. Prominent anti–HIV vaccine conspiracy theories often falsely linked HIV vaccines to concurrent COVID-19 and other HIV-related events.

Conclusions: The results highlight COVID-19 as a significant context for public discourse and reactions regarding HIV vaccines from both positive and negative perspectives. The success of mRNA COVID-19 vaccines shed a positive light on HIV vaccines. However, COVID-19 also situated HIV vaccines in a negative context, as observed in some anti–HIV vaccine conspiracy theories misleadingly connecting HIV vaccines with COVID-19. These findings have implications for public health communication strategies concerning HIV vaccines.

Introduction

Vaccination has long been recognized as a crucial preventive measure against diseases and infections, but opposition to vaccines has endured [ 1 ]. HIV vaccination has been regarded as a potential preventive measure to help combat the HIV epidemic in the United States, with research and progress dating back to the mid-1980s but without success thus far [ 2 ]. An estimated 1.2 million people were living with HIV in the United States by the end of 2021, with 36,136 new HIV diagnoses reported in 2021 [ 3 ].

On January 27, 2022, the biotechnology company Moderna announced the initiation of clinical trials for an HIV vaccine using messenger RNA (mRNA) technology [ 4 ]. In March 2022, the National Institutes of Health announced the start of clinical trials for 3 mRNA HIV vaccines [ 5 ]. The mRNA technology had previously been used in the Pfizer-BioNTech and Moderna COVID-19 vaccines, which protected individuals against severe symptoms and fatalities during the pandemic [ 6 ]. Following the successes of mRNA COVID-19 vaccines, which led to the Nobel Prize in Physiology or Medicine being awarded to 2 scientists in October 2023 [ 7 ], researchers have been investigating the potential of mRNA vaccines for various other diseases, including HIV [ 8 , 9 ]. The announcements of clinical trials for mRNA HIV vaccines revived public discussion on the prospect of vaccines to combat HIV [ 9 ] despite >3 decades of unsuccessful research [ 2 ]. Meanwhile, these announcements were made against the backdrop of intense vaccine debates during the COVID-19 pandemic, with misinformation and conspiracy theories fueling vaccine hesitancy [ 10 - 12 ].

The X platform, formerly known as Twitter, has been a significant social media platform and a vital source for text-based public discourse. Posts on X have been studied to understand public discourse about vaccines in general [ 13 - 15 ] and about specific vaccines, such as COVID-19 vaccines in recent years [ 12 , 16 , 17 ]. However, there is a dearth of research about public discourse on HIV vaccines on social media. Given the advancement in mRNA technology in COVID-19 vaccines and heated vaccine debates, it has become especially important to gain insights into public discourse and reactions regarding potential new vaccines.

This study is grounded in the growing field of infodemiology and infoveillance, which investigates the “distribution and determinants of information in an electronic medium,” specifically on the web, by analyzing unstructured text with the aim of informing public health practices or serving surveillance objectives [ 18 ]. In recent infodemiology and infoveillance studies, machine learning algorithms have been increasingly used to examine substantial amounts of social media content, such as posts on X related to COVID-19 vaccines [ 12 , 16 , 17 ] and HIV prevention [ 19 ], to extract insights into public discourse and reactions. These algorithms automatically analyze extensive volumes of posts and capture latent textual information such as topics and sentiments. This study aimed to investigate how users used different post types to contribute original content to topics and valence identified through machine learning algorithms and how these topics and valence affected user reactions on X regarding HIV vaccines. In addition, by manually coding the most engaged posts, similar to an approach used in previous infodemiology and infoveillance research [ 20 ], the study intended to identify salient aspects of HIV vaccines related to COVID-19 as well as prominent anti–HIV vaccine conspiracy theories. Analyzing posts on X about HIV vaccines can shed light on public discourse and information diffusion. These findings have implications for shaping public health communication strategies about HIV vaccines [ 18 ]. Furthermore, the findings may help in understanding the acceptability of the HIV vaccine upon its successful development in comparison with adherence to existing HIV prevention measures. Previous infodemiology and infoveillance research effectively increased the forecast accuracy of COVID-19 vaccine uptake by leveraging insights derived from posts on X [ 21 ].

Literature Review

Public discourse about hiv prevention on x.

Social media platforms have become important channels for HIV communication, enabling the dissemination of and engagement with content encompassing a wide array of issues related to HIV prevention, treatment, coping, and available resources [ 22 , 23 ]. An earlier infodemiology study examined 69,197 posts on the X platform containing the hashtag #HIVPrevention between 2014 and 2019 and categorized these posts into 10 identified topics concerning HIV prevention [ 19 ]. Among them, pre-exposure prophylaxis had the highest representation with 13,895 posts, followed by HIV testing; condoms; harm reduction; gender equity and violence against women; voluntary medical male circumcision; sex work; postexposure prophylaxis; elimination of mother-to-child transmission of HIV; and abstinence, which had the lowest representation with 180 posts. Furthermore, that study suggested a consistency between the volume of posts related to specific HIV prevention measures on X over time and the temporal trends in the uptake of those measures [ 19 ]. It is noteworthy that the topic of HIV vaccines was absent, which suggested minimal public discourse on the topic during these years. This may be associated with the extensive history of unsuccessful research in this area [ 2 ].

Despite the availability of current HIV prevention measures, efforts have been made to develop HIV vaccines, which are considered necessary to bridge the gap between the challenges in adhering to existing HIV prevention measures and the ambitious goal set by United Nations member states to end the HIV epidemic by 2030 [ 24 , 25 ]. The surge in public discussion about HIV vaccines, possibly elicited by the clinical trials for mRNA HIV vaccines [ 9 ], presented an optimal opportunity to investigate public discourse and reactions regarding HIV vaccines. To the best of our knowledge, this is the first study to analyze posts on X about HIV vaccines.

Public Discourse and Post Types on X

On the X platform, public discourse featuring original content can be observed through 3 post types: self-composed posts, quote posts, and replies [ 26 ]. X users can compose a post. They can also create a quote post, which entails reposting a post while adding their comments. In addition, they can reply to a post to share their comments [ 26 ]. While self-composed posts initiate new conversations, quote posts and replies enable users to join existing conversations by contributing their own comments [ 27 ]. The Pew Research Center’s analysis of survey respondents’ posts on X from October 2022 to April 2023 revealed the composition of different types of posts. Regarding the 3 types of posts containing original content, replies accounted for the highest proportion at 40%, followed by self-composed posts at 15% and quote posts at 9%. The remaining 35% were reposts [ 28 ].

Machine learning algorithms have been increasingly used in recent years to identify latent message features, including textual topics and sentiment valence, among vast numbers of social media posts, as exemplified by previous research analyzing posts on X about COVID-19 vaccines [ 12 , 16 , 17 ] and HIV prevention [ 19 ]. However, the patterns of public discourse in social media conversations are unclear. Specifically, there is a scarcity of research on how people contribute their original content to topics and valence related to a public health issue. This study aimed to address this gap by examining the relationship between post types and message features, specifically topics and valence uncovered using machine learning algorithms, with a focus on HIV vaccines as the subject matter. The findings will advance our knowledge of user contributions to social media conversations about HIV vaccines.

Message Features Influencing User Reactions on X

Examining message diffusion on social media has been a multifaceted challenge, especially with vaccines being a contentious issue debated fervently during the COVID-19 pandemic [ 16 ]. Another contribution of this study is to advance this research area by using machine learning to investigate the synergistic impact of content and account features on user reactions regarding a potential new vaccine amid the context of intense vaccine debates.

The extent to which a message results in optimal diffusion on social media can be gauged by user reactions [ 16 , 29 - 31 ]. On X, a user can engage with posts—be it a self-composed post, quote post, or reply—in 2 primary 1-click reactions: liking and reposting [ 26 ]. An X user can like a post to show appreciation for it or repost it to share it publicly. Compared to liking, reposting is a more social behavior [ 16 , 32 ]. Unlike X’s old timeline, which mostly displayed posts from accounts that a user followed, its current “For you” timeline also shows posts that those accounts have engaged with along with other posts recommended based on user reactions [ 33 ]. The nature of promoting posts based on user reactions makes it more important to investigate the factors that influence user reactions.

This study investigated 2 categories of message-level features that, according to previous research, can drive user interactions: content features in terms of topics and valence and account features in terms of user verification and follower count. Post topics affect likes and reposts on X [ 16 , 30 , 34 ]. Previous research on COVID-19 vaccine posts on X has indicated that posts containing useful information garner more likes and reposts [ 16 ]. This is likely because information utility fills people’s knowledge gaps and serves their utilitarian needs in the face of health risks [ 16 , 32 , 34 - 36 ]. In addition, previous studies have suggested that the novelty of useful information further facilitates sharing of digital health information [ 32 , 36 ], such as updates about COVID-19 vaccine development [ 12 ]. Given the initial success of mRNA technology in COVID-19 vaccines, mRNA HIV vaccine candidates may possess the inherent features of prospective usefulness and ongoing novelty. As a result, posts presenting pertinent information have the potential to generate more likes and reposts. Meanwhile, the announcements of clinical trials for mRNA HIV vaccines were made amidst intense vaccine debates during the COVID-19 pandemic [ 12 ]. Previous research has shown that perceived controversiality in health information increases viewership but not sharing on social media [ 32 ]. In the context of the heated controversy surrounding vaccines, it is crucial to understand user reactions to new potential vaccines.

In addition to post topics, post valence can play a role in user reactions [ 34 ]. Past research has generally revealed that there are more positive than negative posts on X about vaccines in general [ 13 - 15 ] and, more recently, about COVID-19 vaccines in particular [ 12 , 16 , 17 ]. However, the influence of post valence on user reactions remains unclear. One study on COVID-19 vaccines showed that positive posts on X received more likes but not more reposts [ 16 ]. Another study on vaccines regardless of their type revealed that antivaccine posts garnered more reposts than provaccine posts on X [ 13 ]. A psychological rationale supporting the social transmission of positive content is the motivation of individuals to present themselves positively and shape their self-identity [ 35 , 37 ]. In comparison, social transmission of negative content can be attributed to the idea that certain negative content triggers activation, which drives user reactions [ 35 ].

Furthermore, previous research has shown that account features such as verification status and follower count affect user reactions on social media [ 13 , 16 , 34 ]. Given the vast amounts of information available in the digital age, the authenticity of user accounts becomes crucial in the diffusion of health information. One study revealed that account verification enhanced the number of likes and reposts for posts about COVID-19 vaccines on X [ 16 ]. Another study indicated that follower counts increased the number of reposts for posts about vaccines on X regardless of vaccine type [ 13 ].

Conspiracy Theories

A conspiracy theory refers to the belief that a coalition of powerholders forms secret agreements with malevolent intentions [ 38 , 39 ]. It differs from other types of misinformation by hypothesizing a pattern in which people, objects, or events are interconnected in a causal manner [ 39 ]. Previous research has revealed conspiracy theories as a salient theme in antivaccine discourse on social media, along with other themes such as side effects and inefficacy [ 40 , 41 ]. For HIV vaccines, conspiracy theories are crucial in understanding public discourse against them given the limited information about side effects and inefficacy until future success. An additional contribution of this study is the identification of prominent anti–HIV vaccine conspiracy theories through manual coding of the most engaged with negative posts.

Antivaccine conspiracy theories contribute to vaccine hesitancy [ 42 - 44 ], as observed recently with COVID-19 vaccines [ 10 , 11 ]. Understanding the themes and reasoning behind antivaccine conspiracy theories will provide vital implications for deploying evidence-based and logic-driven strategies to counter them [ 45 - 47 ]. A systematic review of antivaccine discourse on social media from 2015 to 2019 revealed pre–COVID-19 conspiracy theories [ 41 ]. These theories claimed that powerholders promoted vaccines for self-serving interests, including hiding vaccine side effects for financial gain and controlling society and the population [ 40 , 41 ]. During the COVID-19 pandemic, antivaccine conspiracy theories thrived on social media. Some theories claimed that the pandemic was invented for pharmaceutical companies’ profit from vaccines [ 44 ], whereas others linked mRNA COVID-19 vaccines to infertility and population control [ 10 , 11 , 44 , 48 , 49 ]. Another conspiracy theory claimed that Bill Gates and the US government aimed to implant trackable microchips into people through mass vaccination [ 11 , 27 , 49 ]. This aligns with conspiracy theories from earlier years. In particular, the Big Pharma conspiracy theory claims that pharmaceutical companies, together with politicians and other powerholders, conspire against the public interest [ 50 ]. The New World Order conspiracy theory alleges that a power elite with a globalization agenda colludes to rule the world [ 51 ]. Conspiracy theories have also linked other vaccines, such as poliovirus vaccines in the past [ 52 , 53 ] and COVID-19 vaccines in recent years, to HIV infection [ 54 , 55 ]. These conspiracy theories were based on the claims that alleged vaccines contained HIV.

Research Questions

To understand public discourse and reactions surrounding HIV vaccines on the X platform, we put forward the following research questions (RQs):

• What are the topics of the posts about HIV vaccines? (RQ 1)
• What is the valence of the posts about HIV vaccines? (RQ 2)
• How do topics and valence vary across different types of posts? (RQ 3)
• How do content features (topics and valence) and account features (verification status and follower count) affect 1-click reactions in terms of likes and reposts, respectively? (RQ 4)
• What are the prominent anti–HIV vaccine conspiracy theories that receive the most reactions? (RQ 5)

Data Source

We collected English-language original posts about HIV vaccines on the X platform from January 1, 2022, to December 31, 2022, using Netlytic [ 56 ]. The selected time frame began in January 2022 with the initiation of mRNA HIV vaccine clinical trials fueling public discussion and concluded in December 2022, a significant month for HIV and AIDS awareness marked by World AIDS Day on the first day of the month. Posts, excluding reposts, that contained both keywords (case insensitive)—“HIV” and “vaccine”—were extracted, resulting in a total of 36,424 posts across 365 days. Posts were collected weekly. Posts published from the last ending time point to at least 24 hours before each collection time point were included in the data set, allowing for a substantial reaction time.

The unit of analysis was a post. For each post, automated extraction produced data for user reactions (the number of likes and reposts) as well as account features (account verification status and follower count). All 36,424 posts underwent topic modeling using latent Dirichlet allocation (LDA) to identify latent topics, as well as sentiment analysis using Valence Aware Dictionary and Sentiment Reasoner (VADER) to access valence. LDA generated topic-specific loadings and identified the dominant topic for each post. VADER generated a valence compound score for each post, which was also categorized as positive, neutral, or negative based on standard VADER classification values.

LDA revealed 3 topics. As the topic of HIV and COVID-19 dominated in a large proportion of posts, we manually coded the 1000 most engaged posts containing the words “HIV” and “COVID” to uncover the salient aspects of HIV vaccines related to COVID-19. To develop coding for subtopics, 2 researchers initially reviewed and coded the top 200 posts with the most reactions. Subtopics were categorized by adapting existing categories from the literature [ 16 , 34 ] and integrating newly identified subtopics from the posts. The Scott π was 0.80 for categorizing subtopics. Subsequently, each researcher independently coded half of the remaining 800 posts.

We then conducted cross-tabulation analyses among all posts to examine the distribution of topics and valence among different types of posts. Furthermore, we conducted linear regression analyses among all posts to assess the influence of content and account features on these 1-click reactions. Of all 36,424 posts, 19,284 (52.94%) received ≥1 like, and 9155 (25.13%) received ≥1 repost. We added a constant value of 1 to all data points for likes and reposts before applying the natural logarithm. This was done to include posts with 0 likes or reposts and to mitigate the skewness of the data distribution.

Of the 28,439 posts that received likes or reposts, 6176 (21.72%) were negative. We manually coded the top 1000 negative posts with the most reactions to uncover prominent anti–HIV vaccine conspiracy theories. To develop coding for conspiracy theories, 2 researchers initially reviewed and coded the top 200 negative posts that received the most reactions. Posts containing conspiracy theories were identified based on expressions of postulated causal connections between people, objects, or events with malevolent intent [ 38 , 39 ]. Conspiracy theories were then classified based on the existing ones from the literature [ 50 , 51 ] and the emerging ones observed in the posts. Coding discrepancies were resolved through a further review of questionable posts and refinement of the conspiracy theories following the approach used in previous social media content analyses [ 40 , 57 ]. The procedure identified conspiracy theories and established intercoder reliability. The Scott π was 0.83 for identifying conspiracy theories and 0.81 for categorizing them. Each researcher then independently coded half of the remaining 800 negative posts.

User Reactions

One-click reactions were measured by the number of likes and reposts, which were automatically extracted. Because a small number of posts garnered significant 1-click reactions, the distribution of likes and reposts was right skewed. To reduce right skewness, we used the natural logarithm of the number of likes and reposts in linear regression analyses, as done in previous research [ 16 , 30 , 34 ].

Post Topics

All posts underwent topic modeling using LDA [ 58 ]. Topic modeling is a commonly used unsupervised learning method that generates a probabilistic model for a corpus of text data [ 59 ]. As a widely used topic model [ 59 ], LDA has been applied to discover topics within rich sources of digital health information, such as electronic health records [ 60 ], reviews on the web [ 61 ], and posts on X [ 16 , 34 ].

LDA relies on 2 matrices to define the underlying topical structure: the word-topic matrix and the document-topic matrix [ 62 ]. In this study, a post was considered a document. The general idea is that a post is represented by a Dirichlet distribution of latent topics, with each latent topic being represented by a Dirichlet distribution of words [ 59 ]. In the word-topic matrix, where the rows represent words and the columns represent topics, each element reveals the conditional probability of a word appearing within a topic [ 62 ]. A topic can be interpreted by examining a list of the most probable words ranked by their frequencies within a given topic using 3 to 30 words [ 63 ]. In the document-topic matrix, where rows represent posts and columns represent topics, each element reveals the conditional probability of a topic underlying a post [ 62 ]. In other words, it reveals the topic-specific loadings for each post.

When interpreting each topic, we reviewed the word-topic matrix as well as sample posts with high topic-specific loadings and significant reactions. LDA generated topic-specific loadings for each post ranging from 0 to 1, with values closer to 1 indicating a higher probability of a topic being associated with a post. Furthermore, LDA determined the dominant topic for each post by selecting the topic with the highest topic-specific loading among all topics. In the cross-tabulation analysis examining the distribution of topics across post types, the dominant topic for each post was entered for analysis. In the linear regression models assessing message-level drivers of user reactions, topic-specific loadings for each post were entered as topic values following previous research [ 16 , 34 ].

Post Valence

We used VADER to analyze the sentiment valence of each post. VADER is a rule-based model specifically attuned for assessing sentiments expressed in social media text [ 64 ]. VADER generated a compound valence score for each post ranging from –1 to 1, with a value of –1 indicating the most negative sentiment and a value of 1 indicating the most positive sentiment [ 65 ]. The standard VADER compound value thresholds for classifying valence categories are as follows: 0.05 to 1 for positive, −0.05 to 0.05 for neutral, and −0.05 to −1 for negative [ 65 ]. In the cross-tabulation analysis examining the distribution of valence among post types, the valence category for each post was entered for analysis. In the linear regression models assessing message-level drivers of user reactions, the VADER compound valence score for each post was used.

This study collected original posts excluding reposts. For each original post, it was automatically extracted whether it was a self-composed post, a quote post with comments, or a reply.

In total, 2 researchers manually coded the top 1000 out of 6176 negative posts with the highest total number of likes and reposts to uncover highly engaged conspiracy theories. They distinguished conspiracy theories from other types of negative information, particularly other types of misinformation, by recognizing the presence of a hypothesized pattern of causal connections between people, objects, or events for malicious intent [ 38 , 39 ]. Conspiracy theories were then categorized based on the existing ones from the literature and the emerging ones observed in the posts.

As an example, consider a post paraphrased as follows:

Image using condoms consistently, only to contract HIV from a COVID vaccine.

It was posted on February 9, 2022, and received 783 likes and 296 reposts. This post was not coded as displaying a conspiracy theory as it only presented misinformation suggesting that COVID-19 vaccines caused HIV. In comparison, another post was paraphrased as follows:

The COVID vaccine contained a spike protein derived from HIV. I was banned from saying this and ridiculed for months. Also, pharmacies stock up HIV self-tests.

It was posted on February 8, 2022, with 147 likes and 48 reposts. This post was coded as displaying a conspiracy theory. It was further classified within the category of conspiracy theories linked to COVID-19 vaccines containing, causing, or increasing HIV. This post suggested a hypothesized pattern of maliciously intended causal connections between the claim that the COVID-19 vaccine contained HIV and the stocking of HIV self-tests in pharmacies. As another example, a post was paraphrased as follows:

Scientists uncover a “highly virulent” strain of HIV in the Netherlands.

It was posted on February 12, 2022, and received 11 likes and 11 reposts. This post conveyed negative information but did not present a conspiracy theory. In comparison, another post was paraphrased as follows:

By coincidence again, the development of a new mRNA HIV vaccine began just before the emergence of the new HIV strain.

It was posted on February 8, 2022, and received 102 likes and 4 reposts. This post was coded as presenting a conspiracy theory and further classified into the category of conspiracy theories linked to the identification of a new highly virulent HIV strain. This post emphasized the speculative timing of the discovery of the new highly virulent HIV strain occurring shortly after the announcement of the development of a new mRNA HIV vaccine.

Account Features

For each post, the posting account’s verification status and follower count were automatically extracted.

Data Analysis

We used cross-tabulation analyses to investigate the distribution of topics and valence across different post types, in which the dominant topic and valence category for each post were entered, respectively, alongside the post type. We used linear regression models to examine the message-level drivers of user reactions among posts that received likes or reposts. In the linear regression models, a constant value of 1 was added to all data points of like and repost counts. The natural log-transformed values for each post were then regressed on 3 topic-specific loadings generated from LDA, the valence compound score generated from VADER, and 2 autoextracted account features—account verification status and follower count. The “plus one” technique was used to include posts that received 0 likes or reposts and to address the skewness of the data distribution.

Ethical Considerations

Following Long Island University’s institutional review board determination process, an institutional review board review was deemed unnecessary for this study, which collected and analyzed publicly available social media data. All referenced posts were paraphrased to avoid association with any particular user on the X platform.

RQ 1 asked about the topics present in all the posts. We trained a topic model using LDA exploring topic numbers ranging from 2 to 20. The optimal number of topics ( k ) was selected considering both the coherence score ( C v ) and the topic model visualization in a Python library called pyLDAvis , as done in previous research [ 16 , 66 ]. C v is a metric that reflects the semantic coherence of topics by evaluating the word co-occurrence likelihood within topics [ 67 ]. A higher C v indicates a better classification achieved by the topic model. In this study, the model with 2 topics ( k =2) yielded the highest C v (0.42), whereas the model with 3 topics ( k =3) yielded the second highest C v (0.35). The pyLDAvis chart depicts each topic as a circle. Overlapping areas between circles suggest similarities in topics. Thus, a chart without overlapping circles is preferable for k . The pyLDAvis chart for this study showed that, when the value of k was 2 or 3, the circles did not overlap. However, when k reached 4, the circles began to overlap, and overlapping circles persisted for values of k ranging from 4 to 20. Between the k values of 2 and 3, we opted for a model comprising 3 topics ( k =3) considering that a smaller number of topics tends to result in overly broad meanings for each topic [ 68 ].

Table 1 summarizes the 3 topics and lists their representative posts. Each topic was interpreted by examining the top 10 probable words ranked by frequency, along with sample posts exhibiting high topic-specific loadings and 1-click reactions. Topic 1 was HIV and COVID-19, covering 78% of the tokens [ 69 ] and dominating in 92.46% (33,678/36,424) of the posts. Topic 2 was mRNA HIV vaccine trials, covering 14% of the tokens and dominating in 5.91% (2151/36,424) of the posts. Topic 3 was HIV vaccine and immunity, covering 8% of the tokens and dominating in 1.63% (595/36,424) of the posts.

Figure 1 illustrates the daily numbers of original posts about HIV vaccines throughout 2022, in total and categorized into 3 topics. Moderna’s announcement of clinical trials for its first mRNA HIV vaccine on January 27, 2022, likely triggered the initial surge, culminating in a daily peak when the number of posts reached 805 on January 29, 2022. The daily number of posts about mRNA HIV vaccine trials (topic 2) in the week following Moderna’s announcement was higher than on other days throughout the year. Nevertheless, even during that week, there were higher daily numbers of posts about HIV and COVID-19 (topic 1), which remained dominant among the 3 topics during the entire year. The year’s second and highest daily peak occurred on February 8, 2022, recording a total of 1603 posts, most of which focused on HIV and COVID-19 (topic 1). This could be attributed to the emergence of new HIV-related events in early February 2022, including the promotion of HIV tests by public figures [ 64 ] and the discovery of a new highly virulent HIV strain [ 65 ]. The third highest daily peak, comprising 1085 posts, occurred on May 18, 2022, which has marked HIV Vaccine Awareness Day since 1998. Most of the posts centered on HIV and COVID-19 (topic 1). The remainder of the year did not reach such high peaks, with the largest daily volume of 205 posts occurring on December 2, 2022, the day following World AIDS Day, observed since 1988. Similar to previous daily peaks, most of the posts revolved around HIV and COVID-19 (topic 1).

The results revealed the dominance of HIV and COVID-19 (topic 1) in 92.46% (33,678/36,424) of the posts, with HIV as the most frequent word and COVID as the fourth most frequent word. To gain a deeper understanding of salient aspects of HIV vaccines related to COVID-19, we manually coded the top 1000 posts with the highest total number of likes and reposts that contained both HIV and COVID . Table 2 summarizes the subtopics and their representative posts with like and repost counts.

The first major subtopic, comprising 24% (240/1000) of the posts, focused on the reciprocal influence of HIV vaccines and COVID-19 vaccines on each other’s development. Years of HIV vaccine research facilitated the rapid development of mRNA COVID-19 vaccines, and the success of COVID-19 vaccines might accelerate the development of mRNA HIV vaccines. The second major subtopic, comprising 17.6% (176/1000) of the posts, involved comparisons between HIV and COVID-19 in various aspects. Specifically, the development speed of HIV vaccines compared to COVID-19 vaccines was a major point of comparison. In addition, some posts questioned whether potential HIV vaccines could be comparable to COVID-19 vaccines in terms of cost and accessibility during rollout. Others raised concerns about efficacy, safety, and inequality for both vaccines. The third major subtopic, comprising 26.5% (265/1000) of the posts, connected COVID-19 vaccines with HIV. One issue discussed was whether COVID-19 vaccines contained, caused, or increased HIV. Another issue raised was distinguishing between HIV symptoms and COVID-19 vaccine side effects, such as a fabricated condition called VAIDS , short for vaccine-acquired immunodeficiency syndrome. The fourth major subtopic, comprising 13.6% (136/1000) of the posts, featured conspiracy theories that presented hypothesized patterns linking COVID-19, HIV, and their vaccines with malicious intent. Prominent conspiracy theories in this subtopic included connecting misinformation that COVID-19 vaccines contain, cause, or increase HIV with the ongoing development of HIV vaccines; associating HIV and AIDS symptoms with side effects of COVID-19 vaccines; and claiming that COVID-19 originated from unsuccessful HIV vaccine research. As this study also manually coded the 1000 most engaged negative posts to identify prominent conspiracy theories, additional results pertaining to conspiracy theories will be discussed further in another subsection. The remaining posts related to HIV and COVID-19 included those that generally mentioned research on them or made connections without specifying details.

a mRNA: messenger RNA.

a The reaction count is the total number of likes and reposts.

b PrEP: pre-exposure prophylaxis.

c VAIDS: vaccine-acquired immunodeficiency syndrome.

d The categories labeled as “other” contain various topics. Thus, no representative post is displayed.

RQ 2 asked about the sentiment valence present in all the posts. According to the standard VADER classification values, valence is categorized by compound scores as follows: positive (0.05 to 1), neutral (−0.05 to 0.05), and negative (−0.05 to −1) [ 65 ]. On average, all posts had a marginally positive score of 0.053. HIV and COVID-19 (topic 1) had a slightly positive average score of 0.055. The mRNA HIV vaccine trials (topic 2) had a neutral average score of 0.040, leaning toward the positive side. HIV vaccine and immunity (topic 3) had a more neutral average score of −0.0008. Moreover, 42.78% (15,584/36,424) of the posts were positive, 25.64% (9338/36,424) of the posts were neutral, and 31.58% (11,502/36,424) of the posts were negative.

Topics and Valence Across Post Types

Of the 36,424 posts, 18,580 (51.01%) were replies, making up over half of the overall count. Self-composed posts totaled 41.6% (15,151/36,424), whereas the remaining 7.39% (2693/36,424) were quote posts. RQ 3 asked about the distribution of topics and valence among the 3 post types. As Table 3 shows, the distribution of topics varied by post type (N=36,424, χ 2 4 =2511.4, P <.001). Of the self-composed posts, 85.36% (12,933/15,151) focused on HIV and COVID-19 (topic 1) and 13.21% (2001/15,151) focused on mRNA HIV vaccine trials (topic 2). In comparison, quote posts and replies exhibited a different pattern, in each case >97% of posts centering on HIV and COVID-19 (topic 1; 2616/2693, 97.14% and 18,129/18,580, 97.57%, respectively).

As Table 4 shows, the distribution of valence also varied by post type (N=36,424, χ 2 4 =911.7, P <.001). The proportion of positive posts was slightly higher among self-composed posts at 44.95% (6810/15,151) compared to replies at 41.09% (7634/18,580) and quote posts at 42.33% (1140/2693). Self-composed posts had a smaller proportion of negative posts at 23.56% (3570/15,151) compared to replies at 37.64% (6994/18,580) and quote posts at 34.83% (938/2693). The proportion of neutral posts was larger for self-composed posts at 31.49% (4771/15,151) compared to quote posts at 22.84% (615/2693) and replies at 21.27% (3952/18,580).

Regarding the distribution of topics and valence among the 3 types of posts, quote posts and replies displayed similarities, whereas self-composed posts diverged. Compared to self-composed posts, which initiate new conversations, there was a higher proportion of HIV and COVID-19-related posts (topic 1) and a greater proportion of negative posts among quote posts and replies, which contribute to existing conversations.

a N=36,424, χ 2 4 =2511.4, P <.001.

b mRNA: messenger RNA.

a N=36,424, X 2 4 =911.7, P <.001.

Content and Account Features Influencing User Reactions

RQ 4 asked about the influence of content and account features on likes and reposts.

Liking is more common than reposting. While 52.94% (19,284/36,424) of posts received an average of 24.83 likes, ranging from 1 to 102,843, a total of 25.13% (9155/36,424) posts received an average of 11.38 reposts, ranging from 1 to 10,572. Table 5 reveals the influence of content features (topics and valence) and account features (verification status and follower count) on the natural log-transformed number of likes and reposts. Both linear regression models were significant at P <.001. The adjusted  R 2 was 0.072 for the like model and 0.090 for the repost model.

Among the 3 topics identified using LDA, HIV and COVID-19 (topic 1) did not affect like counts but decreased repost counts. In comparison, mRNA HIV vaccine trials (topic 2) decreased like counts while increasing repost counts. Positive valence increased like and repost counts. Account verification status and follower count increased like and repost counts.

a The natural logarithm, ln (Y i +1), was calculated on like and repost counts. This transformation was conducted to include posts receiving 0 likes and reposts, as well as to account for the skewness of the data distribution.

b F (model significance): P <.001; adjusted R 2 =0.072.

c F (model significance): P <.001; adjusted R 2 =0.090.

d mRNA: messenger RNA.

e The models excluded topic 3 on HIV vaccine and immunity to address multicollinearity issues arising from its correlations with topics 1 and 2. The reported standard β for topic 3 represents a possible β value if it had been included in the models.

Posts With Most Reactions

Table 6 summarizes posts ranked within the top 5 for the number of likes and reposts presented in chronological order. It is worth noting that all posts in the top 5 for likes and reposts were self-composed. One particular post, which garnered the most likes (n=102,843) and reposts (n=10,572), expressed the incredible feeling of witnessing the development of an HIV vaccine within our lifetimes. It was posted by an unverified account on January 28, 2022, the day after Moderna’s announcement of clinical trials for its first mRNA HIV vaccine.

a Ranks beyond the fifth were not indicated.

Anti–HIV Vaccine Conspiracy Theories

RQ 5 asked about prominent anti–HIV vaccine conspiracy theories. Of the 1000 negative posts that received the most reactions, 227 (22.7%) contained conspiracy theories. As Table 7 shows, we classified these prominent anti–HIV vaccine conspiracy theories into 4 categories and presented their representative posts and the number of reactions.

The first category, comprising 44.9% (102/227) of the posts, formulated conspiracy theories by connecting COVID-19, COVID-19 vaccines, HIV, and HIV vaccines. For instance, 52.9% (54/102) of these posts connected the misinformation regarding COVID-19 vaccines containing, causing, or increasing HIV with the ongoing efforts to develop HIV vaccines. This misinformation may have arisen from past occurrences resurfacing following Moderna’s initiation of its mRNA HIV vaccine trials. One incident occurred at the end of 2020, when an Australian COVID-19 vaccine, which used a small fragment of protein from HIV to clamp SARS-CoV-2’s spike proteins, was abandoned due to false HIV-positive results [ 70 ]. Another incident occurred in October 2020, when 4 researchers sent a letter to a medical journal expressing concerns about the potential increased risk of HIV acquisition among men receiving COVID-19 vaccines using adenovirus type-5 vectors without supporting data from COVID-19 vaccines [ 71 ]. The misinformation typically interpreted the incidents out of context and generally suggested that COVID-19 vaccines contained, caused, or increased HIV without specifying details. In addition, there were conspiracy theories linking HIV and AIDS to COVID-19 vaccine side effects, including a fabricated condition known as VAIDS. VAIDS falsely suggests that COVID-19 vaccines caused immune deficiency [ 72 ]. Furthermore, there were claims that COVID-19 originated from unsuccessful HIV vaccine research.

The second category, comprising 38.3% (87/227) of the posts, suggested that the alignment of concurrent events with Moderna’s start of mRNA HIV vaccine trials in late January 2022 was intentional to manipulate the market for HIV vaccines. These events included the rising HIV discussion and fear; promotion of HIV tests by public figures [ 73 ]; the discovery of a new highly virulent HIV strain [ 74 ]; and the passing away of HIV researchers, including Luc Montagnier, codiscoverer of HIV with an antivaccine stance during the COVID-19 pandemic [ 75 ], all occurring in early February 2022.

The third category, with 11.5% (26/227) of the posts, revealed conspiracy theories based on the distrust of powerholders [ 76 ]. Some posts extended existing conspiracy theories, such as the Big Pharma conspiracy theory [ 50 ] and the New World Order conspiracy theory [ 51 ], into the context of HIV vaccines, emphasizing the intent of powerholders, including major pharmaceutical companies and governments, behind vaccine promotion for financial profits and society control. Other posts created conspiracy theories about the government’s research on HIV vaccines. The remaining posts generally stated that HIV vaccines were a scam. The final category comprised the remaining 5.3% (12/227) of the posts with other conspiracy theories.

It is worth noting that, of the 227 posts containing conspiracy theories, 39 (17.2%) were posted by accounts that had already been suspended at the time of manual coding. For these posts, the X platform displays the following message—“This post is from a suspended account”—and the content of the post is not visible. The X platform suspends accounts that violate its rules [ 77 ]. However, specific details of the violations are not accessible on the platform. The invisibility of these posts halted their spread when the suspension was enacted. For our manual coding of these posts, we used the text obtained during the data collection process.

b The posts were from suspended accounts.

d The categories labeled as “other” contain various conspiracy theories. Thus, no representative post is displayed.

Principal Findings

This study investigated the patterns of public discourse and the message-level drivers of user reactions on the X platform regarding HIV vaccines through the analysis of posts using machine learning algorithms. We examined the distribution of topics and valence across different post types and assessed the influence of content features (topics and valence) and account features (account verification status and follower count) on like and repost counts. In addition, we manually coded the 1000 most engaged posts about HIV and COVID-19 to understand the salient aspects of HIV vaccines related to COVID-19 and the 1000 most engaged negative posts to identify prominent anti–HIV vaccine conspiracy theories.

The results revealed that COVID-19 plays a substantial role as a context for public discourse and reactions regarding HIV vaccines. Of the 3 topics identified using LDA, the leading topic was HIV and COVID-19, covering 78% of tokens and dominating in 92.46% (33,678/36,424) of the posts. Furthermore, on each of the top 4 days with the highest post counts, most of the posts were about HIV and COVID-19. This comprehensive topic included important subtopics that linked HIV vaccines with COVID-19 vaccines, as demonstrated through the manual coding of the 1000 most engaged posts about HIV and COVID-19. These subtopics encompassed the reciprocal influence of HIV vaccines and COVID-19 vaccines in advancing each other’s development; comparisons in their development speed; inquiries about the possible alignment of HIV vaccines with COVID-19 vaccines in terms of cost and accessibility during distribution; and concerns about efficacy, safety, and equality for both vaccines.

COVID-19 positioned HIV vaccines in both a positive and negative context. On the one hand, the success of mRNA technology in COVID-19 vaccines [ 6 ] potentially cast mRNA HIV vaccines in a positive light. The topic of HIV and COVID-19 had a marginally positive valence score of 0.055. Moreover, 3 (60%) out of the 5 most liked posts and 2 (40%) out of the 5 most reposted posts expressed excitement about advancements in HIV vaccines that were based on the experience with COVID-19 vaccines. On the other hand, antivaccine discourse, including conspiracy theories, heated up during the COVID-19 pandemic [ 10 , 11 , 27 , 44 , 48 , 49 ], which posed challenges to HIV vaccines. Of the 1000 most engaged posts about HIV and COVID-19, a total of 136 (13.6%) featured conspiracy theories. Of the 1000 most engaged negative posts, 227 (22.7%) contained conspiracy theories, with 102 (44.9%) of them revolving around HIV and COVID-19. For instance, a prominent conspiracy theory connected the misinformation about COVID-19 vaccines containing, causing, or increasing HIV infection [ 55 ] with the initiation of clinical trials for mRNA HIV vaccines [ 4 , 5 ], implying a malevolent intent behind the deliberate connection. The results indicate that conspiracy theories tend to elicit an approach-oriented response, as evidenced by people engaging in liking and reposting, as opposed to an avoidance-oriented approach [ 39 ]. This underscores the need to intensify efforts to counter conspiracy theories in public health communication about HIV vaccines.

According to a study conducted by the Pew Research Center, irrespective of the subject matter, replies constituted the largest portion of original posts on X, followed by self-composed and quote posts [ 28 ]. Specifically, the number of replies was 3 times greater than that of self-composed posts. In this study, although replies constituted slightly more than half (18,580/36,424, 51.01%) of the posts, it is worth noting that the subject of HIV vaccines elicited a higher proportion of self-composed posts at 41.6% (15,151/36,424). Specifically, the number of replies was 23% higher than that of self-composed posts. Moreover, the topic of mRNA vaccine trials was most evident in self-composed posts compared to replies and quote posts. In comparison, there was a higher proportion of focus on the topic of HIV and COVID-19 and a greater proportion of negative posts among quote posts and replies, which contribute to existing conversations. This suggests that users were more likely to initiate new conversations rather than joining existing conversations about mRNA HIV vaccines. In contrast, they were more likely to join existing conversations rather than starting new conversations about HIV and COVID-19. In addition, users were less likely to initiate new conversations negatively but more likely to contribute negatively to existing ones.

As the primary topic, HIV and COVID-19 had no impact on like counts but had a negative impact on repost counts. In comparison, the topic of mRNA HIV vaccine trials had a negative impact on like counts and a positive impact on repost counts. The results should be interpreted while considering that, as revealed in previous research [ 16 , 34 ] and this study, most posts on the X platform are unlikely to receive likes and even less likely to receive reposts. In this study, among the total of 36,424 posts, approximately half (n=19,284, 52.94%) received likes, and approximately one-quarter (n=9155, 25.13%) received reposts. To include all posts and mitigate the data distribution skewness in the linear regression analysis, we applied the “plus one” technique. This involved adding a constant value of 1 to all like and repost data points before taking the natural logarithm. Although most posts were not liked or reposted, it is noteworthy that the topic of mRNA HIV vaccines led to an increase in repost counts, highlighting its positive influence on social sharing. In addition, 2 (40%) out of the 5 most reposted posts were about mRNA HIV vaccine trials. These results correspond to the findings of previous research that suggested the diffusion of novel useful information [ 12 , 16 , 32 , 36 ].

The overall valence of the posts about HIV vaccines was marginally positive. The positivity aligns with the positive sentiment found in posts on X about vaccines in general [ 13 - 15 ] and COVID-19 vaccines in particular [ 12 , 16 , 17 ]. However, the positivity about HIV vaccines was not apparent as the average score of 0.053 placed it on the edge of the neutral range, which goes from −0.05 to 0.05 according to the standard VADER classification values. Positive sentiment had a favorable impact on like and repost counts, partially consistent with findings of previous research on COVID-19 vaccines [ 16 ]. The post that achieved the most likes conveyed the incredible feeling of witnessing the development of an HIV vaccine in our lifetimes. This could be attributed to the psychological rationale that social transmission of positive content fulfills people’s motivation to present a positive image [ 35 , 37 ]. In alignment with the findings of previous research [ 13 , 16 , 34 ], account verification status and follower count increased like and repost counts.

This study has implications for public health communication related to HIV vaccines and potentially other vaccines. Given the massive scale of the COVID-19 vaccination campaign, it is understandable that people will draw comparisons with other vaccines. Topic modeling identified HIV and COVID-19 as the primary topic, and manual coding revealed various intertwined aspects. Leveraging the advantages observed in the COVID-19 vaccine campaign, such as its widespread accessibility, could be valuable. Furthermore, addressing common concerns such as efficacy, safety, and inequality could also prove beneficial.

In the case of HIV vaccines, it is essential to tackle concerns associated with COVID-19 vaccines, especially those related to HIV vaccines. A major subtopic of HIV and COVID-19 involved suspicions about COVID-19 vaccines containing, causing, or increasing HIV. Another major subtopic was the confusion between HIV symptoms and the alleged side effects of COVID-19 vaccines, such as VAIDS. Misinformation concerning both subtopics has been woven into conspiracy theories, further complicating this situation. To combat misinformation and conspiracies that have these elements, efforts could focus on promoting evidence-based factual information [ 45 - 47 ].

Another notable technique in the conspiracy theories was linking concurrent COVID-19 and other HIV-related events in unsubstantiated relationships to create false perceptions, suggesting that these events were intentional to manipulate the market for HIV vaccines. These HIV-related events included rising HIV discussion and fear, promotion of HIV tests by public figures [ 73 ], the discovery of a new highly virulent HIV strain [ 74 ], and the passing away of HIV researchers, all occurring in early February 2022. These findings suggest that refuting false connections among such concurrent events can be an effective strategy to counter these conspiracy theories [ 45 - 47 ]. These occurrences, frequently entwined within conspiracy theories, could be specifically addressed in public health communication efforts.

Limitations

This study has several limitations. Because we used autoidentified content features (topics and valence) and autoextracted account features (verification status and follower count) in the regression models to predict the autoextracted number of user reactions (likes and reposts), the results were mostly limited to the examined autoidentified and autoextracted factors. For instance, political polarization, which manifested in a wide range of issues, including response to vaccines [ 78 ], could be a factor worth investigating in future studies. Furthermore, manual coding of conspiracy theories revealed a prevalent technique of twisting concurrent events into false relationships. This underscores the significance of refuting unfounded associations among these incidents to counter such conspiracy theories. It will be interesting for future research to assess the impact of this technique on user reactions to conspiracy theories. These findings could provide further insights into public health communication strategies to combat conspiracy theories.

Conclusions

The results highlight COVID-19 as a significant backdrop for public discourse and reactions on the X platform regarding HIV vaccines. COVID-19 situated HIV vaccines in both a positive and negative context. The success of mRNA COVID-19 vaccines shed a positive light on HIV vaccines. However, COVID-19 also situated HIV vaccines in a negative context, as evident in anti–HIV vaccine conspiracy theories falsely linking HIV vaccines to COVID-19. The findings provide implications for public health communication strategies concerning HIV vaccines.

Acknowledgments

This study was supported in part by the College of Arts and Sciences and the Harrington School of Communication and Media at the University of Rhode Island. The authors express their appreciation for the support. The funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

Data Availability

The data sets collected and analyzed during this study are available from the corresponding author upon request.

Conflicts of Interest

None declared.

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Abbreviations

Edited by G Eysenbach; submitted 04.10.23; peer-reviewed by X Ma, J Zhang; comments to author 18.10.23; revised version received 08.11.23; accepted 28.02.24; published 03.04.24.

©Jueman M Zhang, Yi Wang, Magali Mouton, Jixuan Zhang, Molu Shi. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 03.04.2024.

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Toroidally focused ultrasonic flaw detectors

• Acoustic Methods
• Published: 28 July 2011
• Volume 47 , pages 308–310, ( 2011 )

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• A. V. Shevelev 1 &
• Zh. V. Zatsepilova 2  

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New-type toroidally focused ultrasonic flaw detectors, whose application provides an appreciable increase in the flaw detection rate with retention of high sensitivity to flaws, are considered. The construction of a flaw detector is presented, the sizes of a gauge for the formation of the toroidal surface of a lens are given, and the technology of the manufacturing of a toroidal lens is described.

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Ermolov, I.N., Aleshin, N.P., and Potapov, A.I., Nerazrushayushchii control’ (Nondestructive Testing), book 2: Akusticheskie metody kontrolya (Acoustic Testing), Moscow: Vysshaya shkola, 1991.

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Elektrostal Polytechnic Institute, Branch of the National University of Science and Technology “MISIS”, ul. Pervomaiskaya 7, Elektrostal, Moscow oblast, 144000, Russia

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Original Russian Text © A.V. Shevelev, Zh.V. Zatsepilova, 2011, published in Defektoskopiya, 2011, Vol. 47, No. 5, pp. 19–22.

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Shevelev, A.V., Zatsepilova, Z.V. Toroidally focused ultrasonic flaw detectors. Russ J Nondestruct Test 47 , 308–310 (2011). https://doi.org/10.1134/S1061830911050093

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30 Best universities for Mechanical Engineering in Moscow, Russia

Updated: February 29, 2024

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Below is a list of best universities in Moscow ranked based on their research performance in Mechanical Engineering. A graph of 269K citations received by 45.8K academic papers made by 30 universities in Moscow was used to calculate publications' ratings, which then were adjusted for release dates and added to final scores.

We don't distinguish between undergraduate and graduate programs nor do we adjust for current majors offered. You can find information about granted degrees on a university page but always double-check with the university website.

1. Moscow State University

For Mechanical Engineering

2. Bauman Moscow State Technical University

3. National Research University Higher School of Economics

4. Moscow Aviation Institute

5. N.R.U. Moscow Power Engineering Institute

6. National Research Nuclear University MEPI

7. National University of Science and Technology "MISIS"

8. Moscow Institute of Physics and Technology

9. Moscow State Technological University "Stankin"

10. RUDN University

11. Moscow Polytech

12. Moscow State University of Railway Engineering

13. Finance Academy under the Government of the Russian Federation

14. Moscow Medical Academy

15. Russian State University of Oil and Gas

16. mendeleev university of chemical technology of russia.

17. Russian National Research Medical University

18. Plekhanov Russian University of Economics

19. National Research University of Electronic Technology

20. Moscow State Pedagogical University

21. Russian Presidential Academy of National Economy and Public Administration

22. State University of Management

23. Moscow State Institute of International Relations

24. Russian State Geological Prospecting University

25. russian state agricultural university.

26. New Economic School

27. Moscow State Technical University of Civil Aviation

28. Russian State University for the Humanities

29. Russian State Social University

30. Moscow State Linguistic University

Universities for Mechanical Engineering near Moscow

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40 facts about elektrostal.

Written by Lanette Mayes

Modified & Updated: 02 Mar 2024

Reviewed by Jessica Corbett

Elektrostal is a vibrant city located in the Moscow Oblast region of Russia. With a rich history, stunning architecture, and a thriving community, Elektrostal is a city that has much to offer. Whether you are a history buff, nature enthusiast, or simply curious about different cultures, Elektrostal is sure to captivate you.

This article will provide you with 40 fascinating facts about Elektrostal, giving you a better understanding of why this city is worth exploring. From its origins as an industrial hub to its modern-day charm, we will delve into the various aspects that make Elektrostal a unique and must-visit destination.

So, join us as we uncover the hidden treasures of Elektrostal and discover what makes this city a true gem in the heart of Russia.

Key Takeaways:

• Elektrostal, known as the “Motor City of Russia,” is a vibrant and growing city with a rich industrial history, offering diverse cultural experiences and a strong commitment to environmental sustainability.
• With its convenient location near Moscow, Elektrostal provides a picturesque landscape, vibrant nightlife, and a range of recreational activities, making it an ideal destination for residents and visitors alike.

Known as the “Motor City of Russia.”

Elektrostal, a city located in the Moscow Oblast region of Russia, earned the nickname “Motor City” due to its significant involvement in the automotive industry.

Home to the Elektrostal Metallurgical Plant.

Elektrostal is renowned for its metallurgical plant, which has been producing high-quality steel and alloys since its establishment in 1916.

Boasts a rich industrial heritage.

Elektrostal has a long history of industrial development, contributing to the growth and progress of the region.

Founded in 1916.

The city of Elektrostal was founded in 1916 as a result of the construction of the Elektrostal Metallurgical Plant.

Located approximately 50 kilometers east of Moscow.

Elektrostal is situated in close proximity to the Russian capital, making it easily accessible for both residents and visitors.

Known for its vibrant cultural scene.

Elektrostal is home to several cultural institutions, including museums, theaters, and art galleries that showcase the city’s rich artistic heritage.

A popular destination for nature lovers.

Surrounded by picturesque landscapes and forests, Elektrostal offers ample opportunities for outdoor activities such as hiking, camping, and birdwatching.

Hosts the annual Elektrostal City Day celebrations.

Every year, Elektrostal organizes festive events and activities to celebrate its founding, bringing together residents and visitors in a spirit of unity and joy.

Has a population of approximately 160,000 people.

Elektrostal is home to a diverse and vibrant community of around 160,000 residents, contributing to its dynamic atmosphere.

Boasts excellent education facilities.

The city is known for its well-established educational institutions, providing quality education to students of all ages.

A center for scientific research and innovation.

Elektrostal serves as an important hub for scientific research, particularly in the fields of metallurgy, materials science, and engineering.

Surrounded by picturesque lakes.

The city is blessed with numerous beautiful lakes, offering scenic views and recreational opportunities for locals and visitors alike.

Well-connected transportation system.

Elektrostal benefits from an efficient transportation network, including highways, railways, and public transportation options, ensuring convenient travel within and beyond the city.

Famous for its traditional Russian cuisine.

Food enthusiasts can indulge in authentic Russian dishes at numerous restaurants and cafes scattered throughout Elektrostal.

Home to notable architectural landmarks.

Elektrostal boasts impressive architecture, including the Church of the Transfiguration of the Lord and the Elektrostal Palace of Culture.

Offers a wide range of recreational facilities.

Residents and visitors can enjoy various recreational activities, such as sports complexes, swimming pools, and fitness centers, enhancing the overall quality of life.

Provides a high standard of healthcare.

Elektrostal is equipped with modern medical facilities, ensuring residents have access to quality healthcare services.

Home to the Elektrostal History Museum.

The Elektrostal History Museum showcases the city’s fascinating past through exhibitions and displays.

A hub for sports enthusiasts.

Elektrostal is passionate about sports, with numerous stadiums, arenas, and sports clubs offering opportunities for athletes and spectators.

Celebrates diverse cultural festivals.

Throughout the year, Elektrostal hosts a variety of cultural festivals, celebrating different ethnicities, traditions, and art forms.

Electric power played a significant role in its early development.

Elektrostal owes its name and initial growth to the establishment of electric power stations and the utilization of electricity in the industrial sector.

Boasts a thriving economy.

The city’s strong industrial base, coupled with its strategic location near Moscow, has contributed to Elektrostal’s prosperous economic status.

Houses the Elektrostal Drama Theater.

The Elektrostal Drama Theater is a cultural centerpiece, attracting theater enthusiasts from far and wide.

Popular destination for winter sports.

Elektrostal’s proximity to ski resorts and winter sport facilities makes it a favorite destination for skiing, snowboarding, and other winter activities.

Promotes environmental sustainability.

Elektrostal prioritizes environmental protection and sustainability, implementing initiatives to reduce pollution and preserve natural resources.

Home to renowned educational institutions.

Elektrostal is known for its prestigious schools and universities, offering a wide range of academic programs to students.

Committed to cultural preservation.

The city values its cultural heritage and takes active steps to preserve and promote traditional customs, crafts, and arts.

Hosts an annual International Film Festival.

The Elektrostal International Film Festival attracts filmmakers and cinema enthusiasts from around the world, showcasing a diverse range of films.

Encourages entrepreneurship and innovation.

Elektrostal supports aspiring entrepreneurs and fosters a culture of innovation, providing opportunities for startups and business development.

Offers a range of housing options.

Elektrostal provides diverse housing options, including apartments, houses, and residential complexes, catering to different lifestyles and budgets.

Home to notable sports teams.

Elektrostal is proud of its sports legacy, with several successful sports teams competing at regional and national levels.

Boasts a vibrant nightlife scene.

Residents and visitors can enjoy a lively nightlife in Elektrostal, with numerous bars, clubs, and entertainment venues.

Promotes cultural exchange and international relations.

Elektrostal actively engages in international partnerships, cultural exchanges, and diplomatic collaborations to foster global connections.

Surrounded by beautiful nature reserves.

Nearby nature reserves, such as the Barybino Forest and Luchinskoye Lake, offer opportunities for nature enthusiasts to explore and appreciate the region’s biodiversity.

Commemorates historical events.

The city pays tribute to significant historical events through memorials, monuments, and exhibitions, ensuring the preservation of collective memory.

Promotes sports and youth development.

Elektrostal invests in sports infrastructure and programs to encourage youth participation, health, and physical fitness.

Hosts annual cultural and artistic festivals.

Throughout the year, Elektrostal celebrates its cultural diversity through festivals dedicated to music, dance, art, and theater.

Provides a picturesque landscape for photography enthusiasts.

The city’s scenic beauty, architectural landmarks, and natural surroundings make it a paradise for photographers.

Connects to Moscow via a direct train line.

The convenient train connection between Elektrostal and Moscow makes commuting between the two cities effortless.

A city with a bright future.

Elektrostal continues to grow and develop, aiming to become a model city in terms of infrastructure, sustainability, and quality of life for its residents.

In conclusion, Elektrostal is a fascinating city with a rich history and a vibrant present. From its origins as a center of steel production to its modern-day status as a hub for education and industry, Elektrostal has plenty to offer both residents and visitors. With its beautiful parks, cultural attractions, and proximity to Moscow, there is no shortage of things to see and do in this dynamic city. Whether you’re interested in exploring its historical landmarks, enjoying outdoor activities, or immersing yourself in the local culture, Elektrostal has something for everyone. So, next time you find yourself in the Moscow region, don’t miss the opportunity to discover the hidden gems of Elektrostal.

Q: What is the population of Elektrostal?

A: As of the latest data, the population of Elektrostal is approximately XXXX.

Q: How far is Elektrostal from Moscow?

A: Elektrostal is located approximately XX kilometers away from Moscow.

Q: Are there any famous landmarks in Elektrostal?

A: Yes, Elektrostal is home to several notable landmarks, including XXXX and XXXX.

Q: What industries are prominent in Elektrostal?

A: Elektrostal is known for its steel production industry and is also a center for engineering and manufacturing.

Q: Are there any universities or educational institutions in Elektrostal?

A: Yes, Elektrostal is home to XXXX University and several other educational institutions.

Q: What are some popular outdoor activities in Elektrostal?

A: Elektrostal offers several outdoor activities, such as hiking, cycling, and picnicking in its beautiful parks.

Q: Is Elektrostal well-connected in terms of transportation?

A: Yes, Elektrostal has good transportation links, including trains and buses, making it easily accessible from nearby cities.

Q: Are there any annual events or festivals in Elektrostal?

A: Yes, Elektrostal hosts various events and festivals throughout the year, including XXXX and XXXX.

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Medical technology as a key driver of rising health expenditure: disentangling the relationship

Corinna sorenson.

1 LSE Health, London School of Economics and Political Science, London, UK;

2 European Health Technology Institute for Socioeconomic Research, Brussels, Belgium;

Michael Drummond

3 Centre for Health Economics, University of York, York, UK

Beena Bhuiyan Khan

Health care spending has risen steadily in most countries, becoming a concern for decision-makers worldwide. Commentators often point to new medical technology as the key driver for burgeoning expenditures. This paper critically appraises this conjecture, based on an analysis of the existing literature, with the aim of offering a more detailed and considered analysis of this relationship. Several databases were searched to identify relevant literature. Various categories of studies (eg, multivariate and cost-effectiveness analyses) were included to cover different perspectives, methodological approaches, and issues regarding the link between medical technology and costs. Selected articles were reviewed and relevant information was extracted into a standardized template and analyzed for key cross-cutting themes, ie, impact of technology on costs, factors influencing this relationship, and methodological challenges in measuring such linkages. A total of 86 studies were reviewed. The analysis suggests that the relationship between medical technology and spending is complex and often conflicting. Findings were frequently contingent on varying factors, such as the availability of other interventions, patient population, and the methodological approach employed. Moreover, the impact of technology on costs differed across technologies, in that some (eg, cancer drugs, invasive medical devices) had significant financial implications, while others were cost-neutral or cost-saving. In light of these issues, we argue that decision-makers and other commentators should extend their focus beyond costs solely to include consideration of whether medical technology results in better value in health care and broader socioeconomic benefits.

Introduction

Health care spending has risen at rates greater than gross domestic product in most OECD countries. In 2009, average health spending reached 9.5% of gross domestic product, up from 8.8% in 2008 1 ( Figure 1 ). During the same time period, average per capita spending increased by an average of 3.8% in 2008 and 3.5% in 2009, with public spending on health growing at an even faster rate of 4.8% and 4.1%, respectively. 1 For most countries, rising health expenditure is considered an enduring challenge and one that requires a complex balancing act between cost control, affordable and equitable access to beneficial treatments, and support for innovation.

Total expenditure on health as a percentage of gross domestic product (GDP) (1980–2009).

Notes: *All data from 1980 except for Czech Republic (1990), Hungary (1991), Italy (1998), Poland (1990), and Slovenia (1995); **all data from 2009, except for Portugal (2008).

OECD Health Data 2011. 1

A number of factors have been identified as contributors to spending growth, including the ageing of populations, increased public demand and expectations, personal income growth, rising prices of physician and hospital services (eg, labor costs), and inefficiencies in the organization and payment of care. For example, the growth in life expectancy has had an obvious yet gradual impact on the demand for health care. Although the use of care depends ultimately on the health status of a person and not necessarily on their age cohort(s), elderly people use health care more often and more intensively than younger populations, with a high proportion of costs garnered at the end of life. 2 Longer life spans, in concert with an increase in unhealthy lifestyles, have also contributed to an increased prevalence of disease, particularly chronic conditions such as obesity, diabetes, and cancer. Greater prevalence of chronic conditions are likely to increase spending both through an increase in treated prevalence (ie, number of cases treated) and the cost of treatment (ie, spending per case).

However, analysts often point to advances in medical technology and their diffusion across health systems as the principal driver for burgeoning expenditures. 3 – 10 This paper critically examines this conjecture, based on an analysis of a broad range of existing evidence on the relationship between medical technology diffusion and health expenditure. We strive to focus on medical devices, given the gap in the literature on their role in health expenditures, which has predominantly centered on pharmaceuticals, and because the sector has grown considerably in recent years. Not only are there substantially more medical technologies on the market, but they have grown increasingly sophisticated.

The paper is structured as follows. We first outline the methods used to review the literature, followed by a summary of the available evidence. The paper then discusses the complexities inherent to ascertaining the impact of technology on spending, including some of the methodological challenges associated with demonstrating and quantifying this link. Based on the analysis, we close by arguing that while the impact of medical technology on rising costs is an important concern and area of inquiry, attention should also be focused on exploring whether investments in medical technology result in better value, as measured by therapeutic benefit, cost-effectiveness, and other important outcomes (eg, quality of life, improved productivity) in health care, and under which conditions technologies allow for the most effective and efficient use of available health care resources. We offer some general suggestions for what might be done to support this end.

Materials and methods

A literature review was conducted to explore the current evidence base on the relationship between medical technology and health care expenditures. Unlike previous studies in this area, we considered a wide range of literature to ensure adequate coverage of different methodological approaches and ideological perspectives for assessing this relationship. The categories of literature included in the search and review included general and descriptive analyses, policy analyses, literature reviews, multivariate analyses, cost-effectiveness analyses, and cost impact studies of specific technologies. Table 1 presents and describes the various study types included in the review.

Types of studies included in the review

Key databases, including PubMed, EMBASE, MEDLINE, and EconLit, were searched to identify relevant literature. The search terms employed to identify relevant literature included “technolog* AND (expenditure OR cost) AND (health OR healthcare)”, and “medical AND technolog* AND (expenditure OR cost)”.

To identify relevant cost-effectiveness analyses across all medical technologies would be a considerable task. Therefore, we took advantage of two previously completed systematic reviews of economic evaluations of medical devices in the cardiology and orthopedic sectors, the two most significant markets for therapeutic devices. 11 In these reviews, we used the National Health Service Economic Evaluation Database 12 for the searches, which contains structured reviews (abstracts) of full economic evaluations of health care treatments and programs. In these two previously conducted studies, we employed the following search terms “cardiology”, “cardiac rhythm management”, “cardiovascular”, “coronary stents”, “cardiac resychronization”, “implantable cardioverter defibrillators”, “orthopaedic”, “hip”, “knee”, “shoulder”, “ankle”, “elbow”, “arthroplasty”, and “joint”.

All the relevant abstracts were reviewed, from both the general literature review and the review of National Health Service Economic Evaluation Database. Duplicate and irrelevant abstracts were identified and eliminated. Selected articles were reviewed according to a data extraction form, developed in Microsoft Excel, to standardize the review and facilitate subsequent analysis of the collated information. The categories of information extraction included: basic bibliographic information; publication year; literature type (eg, descriptive analysis); study aims; technologies studied; study setting; methods employed; outcomes across health, costs/expenditures, and cost-effectiveness; and, stated conclusions and implications of the study. The information extracted was then analyzed for key themes across the domains interest (ie, impact of medical technology on costs or spending and factors influencing this relationship, and the methodological challenges noted in measuring such linkages).

A total of 86 studies were included in the review ( Appendix 1 ). Table 2 provides a summary of general study details. The majority (52%) of studies were more qualitative in nature (eg, descriptive and policy analyses, literature reviews), followed by cost-effectiveness (40%) and multivariate (24%) analyses. In addition, the bulk of studies were published within the last ten years (77%) and focused on the US (52%). With the exception of cost-effectiveness and cost impact studies examining specific medical devices, most studies (52%) examined medical technology in general, which in some cases included some combination of drugs, devices, diagnostics, or procedures.

Characteristics of the reviewed literature

Although technological innovation is of great significance in health care 13 , 14 and has been claimed to be a key driver of health spending, the review highlighted that research measuring the potential contributions of medical technology to rising health care costs has been relatively sparse. One possible reason for this neglect, and the predominant reliance on more descriptive or qualitative analyses among available studies, is that technology itself and its possible implications on health expenditures are insufficiently understood. Other reasons center on the often limited data available to explore this relationship and the complexities of measuring such associations, which we discuss further below.

The available evidence that does exist suggests that, in general, new medical technology is an important determinant in rising health care expenditures. Of the studies reviewed that attempted to quantify this relationship, mainly econometric studies, the overall impact (ie, proportion of the cost increase) ranges from approximately 25% to 75%, averaging at about 50% 7 , 9 , 10 , 14 – 19 ( Table 3 ).

Contributions of selected factors to growth in health care spending

While much of the quantitative evidence indeed substantiated the cost-increasing effect of new technology overall, the broader spectrum of evidence (eg, cost-effectiveness studies, descriptive analyses) suggests that the relationship between technological advances and health care expenditure is not straightforward or static. Rather, it should be understood as being complex, with a wide range of potential intervening factors that change and shift the dynamic of the association, depending on the particular circumstances.

For example, this relationship often differed across technologies, with some exerting more upward pressure on health care expenditure than others. Of 16 diseases (and subgroups) studied by Scitovski 20 in a case study, new technology decreased costs in eight cases, increased costs in seven, and in one case had neutral effects. The use of “low-tech” technologies such as electrocardiography, laboratory tests, and x-rays stabilized or increased costs at a moderate pace, while the use of complex or sophisticated technologies and procedures such as cesarean section, new treatment modalities for breast cancer, and coronary bypass surgery substantially increased costs. In another case study, Bryan et al 21 found that technology that introduces computer-based information networks for imaging archiving increased annual hospital costs by 1.8%. A Congressional Budget Office 6 review of the available economic literature highlighted particular areas of technology advancement that has been accompanied by more spending, including revascularization for coronary artery disease, diagnostic imaging, and joint replacement.

Whether a particular technology increases or decreases costs depends on whether a given technology: substitutes for an existing service; expands the number of treatable conditions, in that it allows providers to treat conditions they previously could not treat or could not treat effectively or aggressively; intensifies level of use of the technology for the same condition; impacts the delivery of care (eg, improves the capacity of the system to treat more patients); broadens the definition of diseases; and extends life, for which each patient bears (or induces) additional years of health care consumption. 6 , 17 , 22 – 25

For instance, with regards to increasing the indications and applications of the innovations, the initial use of imaging diagnostics (eg, x-rays, computed tomography, magnetic resonance) was initially targeted to specific organs and functions, but their application has extended to almost every part of the human body, resulting in increased spending. 26 Further, some new technologies may allow lower unit costs (ie, treatment becomes cheaper) or cause less discomfort or complications, thereby offering the potential for cost savings. However, these benefits may lead to increased provision of services to persons who, without the new technology, may not have undergone a particular treatment. Therefore, when the cost savings per case are offset by the increased number of procedures, these technologies will result in increased costs in the aggregate, but will almost certainly also increase the total benefits from the care provided. Chernew et al 27 found this was the case with regard to introduction of laparoscopic techniques at the beginning of the 1990s in the US. Of course, new technology can also help extend life (in patients with life-threatening or chronic conditions), which may result in higher spending due to extended years of health care utilization. However, in parallel, a given technology may also allow individuals to live those additional years with greater quality of life or in an improved health state, which could bring potential cost-savings and/or broader social benefits.

In contrast, those technologies that have a substitutive effect, thereby reducing treatment with older technology, eg, use of percutaneous transluminal coronary angioplasty (PTCA) and coronary artery bypass grafting (CABG), may facilitate reductions in spending, even if treatment expansion follows. 25 , 26 For example, PTCA outcomes improved through the introduction of coronary stents, leading to reduced occurrence of restenosis, heart attacks, emergency CABG, and mortality. Consequently, the procedures had become highly substitutable with CABG and for patients with severe coronary artery disease. While the growth of PTCA resulted in higher costs, this has been offset over time by the substitution of PTCA for CABG. Therefore, by metrics of costs, the diffusion of some new technologies can increase spending rapidly at first as it treats those who went without, and less rapidly over time as technology substitutes for more expensive existing treatments. 28 , 29

Moreover, the impact of technology on costs varies depending on the circumstances (eg, patient population, placement in treatment pathway) within which a given therapy is used. For example, several cost-effectiveness analyses on drug-eluting stents have demonstrated that use of these devices increased per patient treatment costs compared with standard balloon angioplasty. 30 – 32 However, if used in medium-risk to high-risk patients, drug-eluting stents could be cost-neutral 31 , 32 or even cost-saving. 30

There are also organizational, economic, and social considerations that influence the link between new technology and spending and arguably interplay with the aforementioned factors. For example, impacts on costs may be affected by how the technology is administered or impacts the delivery of care, because some innovations may lead to an increased use of medical personnel, material supplies, or training, particularly if they employ a new technique or procedure, while others may reduce staff or time requirements or shift care to less costly settings of care (eg, inpatient to outpatient). 33 , 34 In particular, some technologies may improve the efficiency of care delivery by reducing procedure time, length of stay, or number of hospitalizations, thereby increasing the capacity of the hospital to treat additional patients. Overall costs may rise as a result, but such outlays will likely result in improved health outcomes for a greater number of patients.

Finally, technological advancements may generate consumer demand for care (and, perhaps more intense, costly services, even if not cost-effective), as well as the demand for insurance. 17 , 27 , 35 , 36 At the same time, expanding insurance provides increased incentives to develop new technologies. 22 Some analysts maintain that such incentives contribute to long-term growth in expenditure, because the development costs of these products must be recouped by industry (leading to higher prices). 6 Although much of this evidence originates from the US, Barros and Martinez-Giralt 37 also found that payment systems affect the rate of technology adoption and utilization in European systems. National procurement policies and practices may also influence their diffusion into the health system and the costs associated with adoption. For example, use of more centralized purchasing strategies (eg, local or regional procurement consortiums) or value-based purchasing in Europe and elsewhere, where payment is directly linked to the quality and efficiency of a new technology, may reduce spending. 38 , 39

In relation to this point, there are important differences between countries and their respective health systems that influence the adoption and diffusion of new technologies. For instance, technological change often results from incentives in the health care system. Given that incentive structures differ across countries, technology influences spending differently across jurisdictions. 40 Lambooij et al 41 assert that lower resourced countries encourage diffusion of innovations that enhance efficiency, whereas better resourced jurisdictions encourage diffusion of complex, expensive technologies.

The results of our review suggest that medical technology does have a significant role in health care expenditures, albeit a dynamic and complex one. However, there are limitations to the methodological approaches used in the available published literature, which introduce challenges to arriving at a clear assessment of such dynamics. For example, in terms of quantifying this link, the residual approach 4 , 10 , 17 can yield a reasonable indirect approximation of how medical technology relates to long-term growth in total health expenditure, but it can be sensitive to assumptions regarding the effects of other related factors (eg, personal income, health insurance coverage, technology development) and the dynamics between them. 6 , 18 This frequently leads to an overestimation of the effect of technology on spending. Another common method, ie, the proxy approach, 14 , 15 is only as good as the proxy indicator used to assimilate the impact of technology on spending. The use of time as a proxy measure for technological change, for example, not only captures such changes, but may also encapsulate variations in policy, patient experiences, preference, and expenditures. 15

Another method, ie, the case study approach, 25 , 26 is useful to explain the impact of certain medical technologies on health care costs, but there are problems of sampling and it is difficult to generalize to an aggregate or national level. 6 , 19 Consequently, most analysts using this approach have focused on the most significant conditions (eg, prevalent, contributing to high levels of mortality or disability), such as heart disease. These technical issues also characterize cost-effectiveness and cost impact analyses. 42 – 45

In addition to the limitations noted with individual approaches, there are technical issues shared across the various methods. Firstly, the results are frequently based on aggregate level data that are often subject to potential endogeneity and omit variable bias. 46 Secondly, as intimated above, several of these methods can depend on relatively simplified models dealing with highly complicated and interrelated parameters 16 and can only arrive at conclusions about the collective effect of technology on health care spending, not on the contributions of specific technologies. Different types of technologies (eg, drugs versus medical devices) arguably impact health spending differently, particularly in terms of the associated changes in clinical practice that follow their adoption. For example, a recent study 47 estimated that medical devices account for a relatively small share of national health expenditures (3%–5%), which have risen only slightly over the last 20 years, ie, a trend different from that of pharmaceuticals. Thirdly, across both quantitative and qualitative approaches, capturing the economic (and social) complexities surrounding the use of technology can be challenging, because it generally necessitates a complete understanding of the manner and magnitude of change in the clinical management pathway associated with treatment and follow-up. This process can occur over extended periods of time, and can have varying resource costs that can be both easy and difficult to measure. 48

Available studies are often focused on a narrow time window and the specific duration of the life cycle for a technology. Therefore, results from studies with longer or different time periods could vary. For instance, the price of medical technologies generally decreases over time, which would not be captured in shorter-term studies or those that happen to examine a given technology(s) close to initial launch. similarly, technological advances occur simultaneously with changes in other factors that affect health care spending, such as personal income and health financing systems, which make it difficult to identify causality reliably, and exactly how technology itself affects spending growth. Finally, current methods cannot effectively demonstrate the cost impact that would result if availability of technology were reduced or eliminated. In the short-term, cost-savings may be achieved, while limited access to technology may result in higher costs in the long term due to the presence of disease that was not adequately treated owing to reliance on older, less-effective technologies or a complete lack of viable treatment alternatives.

Therefore, while examining the role of medical technology in rising health expenditures is indeed an important area of inquiry, it is largely an incomplete exercise, due to some of the noted methodological issues, and also because most new technological innovations are cost-increasing. Even if a given technology increases costs, it may increase benefits by an even greater amount. In addition, the same technology, applied in different settings, or in different groups of patients, could be cost-effective in some instances and not in others. Consequently, alongside simply examining costs, it is perhaps more productive to assess whether the additional benefits resulting from the use of the technology justify any increase in costs and under which circumstances technologies deliver greater value in health care. That is, are the resulting spending levels reflected in more effective, cost-effective, and higher quality health care?

Several studies from the review indicate that, on average, increases in spending as a result of technological advances have provided reasonable value. For example, Cutler et al 49 found that from 1960 to 2000, average life expectancy increased by 7 years, 3.5 years of which were attributable to improvements in health care. Comparing the value of a year of life (anywhere from $50,000 to $200,000) with the finding that each year of increased life expectancy cost about $19,900 in health spending, they conclude that the increased spending, on average, had been a worthwhile investment. similar conclusions were arrived at by Cutler and McClellan 25 and Skinner et al 50 in examining technological innovation in cardiac care. The former, for example, demonstrated that the use of new technology helped to increase the average coronary patient’s life expectancy by one year (valued at $70,000 per case), while treatment costs increased $10,000 per case (4.2% per year), for a net benefit of $60,000 per case. In addition, as previously discussed, cost-effectiveness analyses of particular medical devices demonstrate value for money (as measured by cost per quality-adjusted life year) and in some limited cases, cost savings. 30 , 31 , 42 , 51 – 53 On a broader level, Fuchs and Sox 54 surveyed physician perceptions of the importance of and value brought by various areas of technological innovation, with magnetic resonance imaging and computed tomographic scanning, angiotensin-converting enzyme inhibitors, balloon angioplasty, statins, and mammography all rated highly.

Before concluding, it is important to note a few limitations to this study. Firstly, while we strove to select and review only studies focused on medical technology, some studies, particularly certain types of econometric studies, looked at technology collectively. Therefore, for those studies, we were unable to distinguish the relative contribution of different types of technology (eg, drugs versus devices) to the proportion of spending attributed to technological innovation. Secondly, we focused our review of cost-effectiveness studies only on selected cardiology and orthopedic devices. Nevertheless, these particular sectors are arguably important markets and those most likely to have a cost impact.

Major technological advances in medical science have allowed health care providers to diagnose and treat illnesses in ways that were previously impossible. In general, such developments have tended to increase health care spending, which has been seen as an important policy concern, especially considering ever-limited health care budgets.

However, examining the link between medical technology and health expenditures is only one part of the picture. In order to understand better the dynamics between innovation and spending, it is important to assess whether and under what circumstances do investments in medical technology result in better value in health care. As Cutler and McClellan 25 assert, “it does not necessarily follow that technology change is therefore bad … costs of technology need to be compared with benefits before welfare statements can be made”. Given the current global economic situation, it is ever more important to ensure that we are attaining good value for money from the technologies developed.

To be sure, the question of whether medical technologies result in added value to the health care system is, of course, also difficult to answer. It depends on our ability to determine the value of output from the health services sector, and placing a value on longer or better quality of life is difficult to appraise. As a starting point, much more comparative research is needed to understand better which technologies work best and are most cost-effective, and under what circumstances. Indeed, as previously discussed, some of the cost-increasing effects of technology arise from inappropriate use, where new treatments are offered to patients for whom there is none to little clinical benefit. Current efforts to support comparative effectiveness research in the US and health technology assessment in Europe and elsewhere may help to foster these aims. However, it is important to note that medical technologies introduce unique technical challenges to health technology assessment or comparative effectiveness research, so assessment methods should adequately account for or be developed to accommodate such aspects. 55 Moreover, in addressing questions of value, such research should strive, where possible, to assess a broad range of potential benefits beyond clinical or therapeutic benefit, including value for money, higher quality of care, improved quality of life, greater efficiency in care delivery (eg, reduced length of stay, shifting care from inpatient to outpatient settings), and enhanced ability to work or return to work.

If the evidence generated from such research is to have an impact on health care spending, it should be used to inform policy and practice. As such, comparative effectiveness research and health technology assessment should be used to help reward and support the introduction of technologies into practice that confer therapeutic benefit and reasonable value for money, either through use in coverage and payment policies, insurance benefit design, or practice guidelines. Conversely, use of low-value interventions should be disincentivized through disinvestment or limitation on their use. Such strategies should be coupled with a greater emphasis on evidence-based delivery of care (eg, aligning appropriate financial incentives for providers and consumers), which might further reduce expenditure levels if such incentives support greater use of cost-effective services. 6 However, in parallel, it will be important to monitor carefully the impact of such policy levers in order to ascertain the best way to control costs without denying the benefits of new innovation. In addition, such measures need to be coupled with other policies and practices to address some of the other drivers of health spending, including initiatives to support healthy aging and improve coordination of care for the chronically ill. Finally, given our ever-limited health care resources, it would be prudent to debate the opportunity costs of funding new (and increasingly expensive) technologies. Even in cases where medical technologies are cost-effective, available resources may be better allocated to other equally or more cost-effective investments outside of the health care sector, such as the environment or education.

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The authors report no conflicts of interest in this work.

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April 5, 2024

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Video game devotees are much more likely to be working-class than middle-class, says research

by Tony Trueman, British Sociological Association

Adults who play video games daily are much more likely to be working-class than middle-class, new research shows.

Although as teenagers their rates of daily playing were similar, by aged 20 middle- class people were devoting more time to their careers, the study found.

Xiaobin Zhou, Dr. Adrian Leguina and Professor Paula Saukko, from Loughborough University, interviewed 37 gamers and analyzed survey data on 3,357 English people aged 16–34.

From the survey, they found that among people aged 20–24, 8.7% working in higher managerial or professional jobs played video games every day, compared with 20% of people in routine or manual jobs. The figures for 25- to 34-year-olds were 8.7% and 13% respectively.

Xiaobin Zhou told the British Sociological Association's online annual conference held today (Friday 5 April 2024) that after aged 20, "the rate of those playing daily decreased dramatically among the middle-class, which contrasts with the routine-manual group where the decline is considerably less marked."

In his interviews with 37 gamers, he found that "most middle-class and upwardly mobile participants' gaming time gradually decreased because of educational or professional responsibilities. They considered self-control a valuable achievement, and underlined that they had found a balance between gaming as a hobby and normal life."

Their self-disciplined habit was "likely inculcated in higher education institutions and professional workplaces. It affects participants' gaming as well as enhances careers and economic positions."

As middle-class people moved away from their homes to study or work, they gamed less together or switched to solo gaming, which allowed them to fit gaming into their busy everyday routine.

The gaming habits of working-class people changed less when they became adults because their life situation stayed the same.

"Working-class participants, especially in further education or not fully employed, often continued to play more frequently and for longer each session when transitioning to young adulthood.

"Some held negative views about their gaming and considered they probably spent too much time gaming, which might not be healthy, but nonetheless rarely sought to control it. Not adopting such controlled gaming habits might make them acutely conscious or ashamed of their gaming."

The study found that working-class participants, who often remained in the same social circle throughout their lives, stressed the bonding they experienced when they played video games with the same friends. This further encouraged them to play video games.

A working-class interviewee told Xiaobin Zhou that he spent about eight hours a day playing video games, "maybe more, maybe less, depending on how well my gaming session is going. It's probably not a healthy amount, but for me personally I quite enjoy it."

One middle-class participant told him, "I'd love to be able to play more and put more time into it but I know it's not the most important thing in my life at this point, so it's always going to take a back seat to something else."

Xiaobin Zhou said that the research was the first to study the transition from adolescence to young adulthood. "We can see video game studies flourishing during the past two decades, but the impact of social class on video gaming has been frequently overlooked."

The survey recorded data on the careers of 16- to 19-year-olds, a few of whom were in managerial jobs, some by running their own business. Of these, 33% played video games every day, compared with 38% of those in routine or manual jobs.

Half of 16- to 19-year-olds in a higher managerial job never played video games, compared with a third in routine or manual jobs. For later ages the figures were 50%–60% for both classes.

The study analyzed data from the English Taking Part Survey, which is an annual survey representative of the English population conducted by the DCMS. The study analyzed a sub-sample of young respondents grouped in three age categories (16–19, 20–24 and 25–34).

The secondary analysis involved merging data from two waves of the TPS (years 2018–2019 and 2019–2020) on 1,771 out of 8,156 people in TPS 2018–2019 and 1,586 out of 7,483 people in TPS 2019–2020 who fell into the selected age range. The interviews carried out by the three researchers were of people between the ages of 18 to 35 years who frequently played games, mainly recruited online from Facebook groups and sub-Reddits in the UK Midlands.

Provided by British Sociological Association

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