'One size does not fit all': The value of person-centred analysis in health professions education research

Cultivating physician empathy: a person-centered study based in self-determination theory

While physician empathy is a vital ingredient in both physician wellness and quality of patient care, consensus on its origins, and how to cultivate it, is still lacking. The present study examines this issue in a new and innovative way, through the lens of self-determination theory. Using survey methodology, we collected data from N = 177 (44%) students at a Canadian medical school. We then used a person-centered approach (cluster analysis) to identify medical student profiles of self-determination (based on trait autonomy and perceived competence in learning) and how the learning environment impacted empathy for those in each profile. When the learning environment was more autonomy-supportive, students experienced higher satisfaction and lower frustration of their basic psychological needs in medical school, as well as greater empathy towards patients. The translation into increased empathy, however, was only evident among the students with higher self-determination at baseline. Results from this study suggest that autonomy-supportive learning environments will generally support medical students' psychological needs for optimal motivation and well-being, but whether or not they lead to empathy towards patients will depend on individual differences in self-determination. Findings and their implications are discussed in terms of developing theory-driven approaches to cultivating empathy in medical education.

The impact of alcohol on affiliative verbal behavior: A systematic review and meta-analysis

BACKGROUND

Language is a fundamental aspect of human social behavior that is linked to many rewarding social experiences, such as social bonding. Potential effects of alcohol on affiliative language may therefore be an essential feature of alcohol reward and may elucidate pathways through which alcohol is linked to social facilitation. Examinations of alcohol's impact on language content, however, are sparse. Accordingly, this investigation represents the first systematic review and meta-analysis of alcohol's effects on affiliative language. We test the hypothesis that alcohol increases affiliative verbal approach behaviors and discuss future research directions.

METHODS

PsycInfo and Web of Science were systematically searched in March 2023 according to our preregistered plan. Eligible studies included social alcohol administration experiments in which affiliative verbal language was assessed. We present a random-effects meta-analysis that examines the effect of alcohol compared to control on measures of affiliative verbal behavior.

RESULTS

Our search identified 16 distinct investigations (comprising 961 participants) that examined the effect of alcohol on affiliative verbal behavior. Studies varied greatly in methods and measures. Meta-analytic results demonstrated that alcohol is modestly associated with increases in affiliative verbal behavior (Hedges' g = 0.164, 95% CI [0.027, 0.301], p = 0.019). Study quality was rated using an adapted version of the Quality Assessment Tool for Quantitative Studies and did not significantly moderate alcohol's effects.

CONCLUSIONS

This study provides preliminary evidence that alcohol can increase affiliative verbal behaviors. This effect may be an important feature of alcohol reward. Given heterogeneity in study features, low study quality ratings, and limited reporting of effect size data, results simultaneously highlight the promise of this research area and the need for more work. Advances in language processing methodologies that could allow future work to systematically expand upon this finding are discussed.

Toward Multidimensional Message Tailoring to Address COVID-19 and Influenza Vaccine-Hesitancy: A Latent Profile Analysis Approach

Vaccines remain the best strategy as the COVID-19 pandemic enters into later stages and governments begin to shed pandemic-control measures. Vaccine hesitancy continues to be a major obstacle in efforts to end the pandemic. This study reports formative evaluation research that adopted a multidimensional approach using latent profile analysis to audience segmentation and message targeting. Within the framework of the integrated behavioral model, data were collected from a US national survey to explore the dimensions in which vaccine-confident vs. -hesitant individuals differed significantly across the topics of COVID-19 and influenza. Latent profile analyses were performed to identify subgroups and establish measurement invariance between COVID-19 and influenza vaccines. Matching message strategies were proposed for the distinctive characteristics of the subgroups for both topics and to be tested in future research.

Identifying academic motivation profiles and their association with mental health in medical school

INTRODUCTION

Academic motivation (AM), motivation in relation to formal studies that as a construct of the self-determination theory (SDT), is frequently assessed by the Academic Motivation Scale (AMS). However, the scoring of AMS in itself is not fully consistent with the SDT theory as only scores of the subscales can be calculated resulting in seven different score means instead of positioning the individual on the self-determination continuum. There have been few attempts at a person-centered approach to AMS scoring, especially among medical students. Our study aimed to find distinct academic motivation profiles and demonstrate their concurrent criterion validity with mental health variables (psychological distress, life satisfaction) among medical students.

METHODS

The AMS-28 college version, the General Health Questionnaire (GHQ-12), and the Single-Item Measure of Life Satisfaction were administered among medical freshmen. Academic motivation profiles were generated by two methods: 1) two-step cluster analysis, and 2) quantile analysis.

RESULTS

The sample consisted of 189 participants (mean age = 19.38 ± 2.03 years, 72% females). The cluster analyses revealed three fairly distinct profiles of self-determination: 'High' (n = 59; mean im = 5.48 ± 0.60; mean em = 6.07 ± 0.41; mean am = 1.57 ± 0.95), 'Moderate' (n = 111; mean im = 4.5 ± 1.06; mean em = 4.41 ± 0.87; mean am = 1.25 ± 0.36), and 'Low' (n = 19; mean im = 4.22 ± 1.02; mean em = 4.03 ± 1.16; mean am = 3.07 ± 1.30). The creation of deciles allowed the identification of those who were most intrinsically (n = 14, 7.4%), extrinsically (n = 10, 5.3%), and least motivated (amotivated) (n = 18, 9.5%). 'Low' self-determination/amotivation was associated with increased psychological distress and decreased life satisfaction.

CONCLUSION

Our results provide means to position medical students on the SDT continuum based on 'Low', 'Moderate', or 'High' levels of self-determination toward their studies. These AM profiles predict the mental health of medical freshmen, which supports the validity of the outcomes and highlight the risks of amotivation for psychological morbidity. The limitations and implications are discussed.

Doctors' alertness, contentedness and calmness before and after night shifts: a latent profile analysis

BACKGROUND

While night shifts are crucial for patient care, they threaten doctors' well-being and performance. Knowledge of how the impact of night shifts differs for doctors is needed to attenuate the adverse effects of night shifts. This study aimed to obtain more precise insight into doctors' feelings surrounding night shift by: identifying profiles based on doctors' alertness, contentedness and calmness scores before and after night shifts (research question (RQ) 1); assessing how doctors' pre- and post-shift profiles change (RQ2); and determining associations of doctors' demographics and shift circumstances with alertness, contentedness and calmness change (RQ3).

METHODS

Latent Profile Analysis using doctors' pre- and post-shift self-rated alertness, contentedness and calmness scores was employed to identify pre- and post-shift profiles (RQ1). A cross-tabulation revealed pre- and post-shift profile changes (RQ2). Multiple regressions determined associations of demographics (i.e. age, sex, specialty) and night shift circumstances (i.e. hours worked pre-call, hours awake pre-call, shift duration, number of consecutive shifts, total hours of sleep) with alertness, contentedness and calmness change (RQ3).

RESULTS

In total, 211 doctors participated with a mean age of 39.8 ± 10 years; 47.4% was male. The participants included consultants (46.4%) and trainees (53.6%) of the specialties surgery (64.5%) and obstetrics/gynaecology (35.5%). Three pre-shift (Indifferent, Ready, Engaged) and four post-shift profiles (Lethargic, Tired but satisfied, Excited, Mindful) were found. Most doctors changed from Ready to Tired but satisfied, with alertness reducing most. Age, specialty, sleep, shift duration and the number of consecutive shifts associated with alertness, contentedness and calmness changes.

CONCLUSIONS

The results provided nuanced insight into doctors' feelings before and after night shifts. Future research may assess whether specific subgroups benefit from tailored interventions.

Profiles of parents' emotion socialization within a multinational sample of parents

Introduction

Seminal emotion socialization theories classify parents according to two patterns of parent emotion socialization processes: 'emotion coaching' (i.e., parents validate and teach children about emotions) versus 'emotion dismissing' parenting (i.e., parents minimize and dismiss their children's emotions). However, empirical evidence supporting this binary distinction of parents remains limited. Our objective was to investigate whether parents can be differentiated by distinct patterns in their (1) beliefs about children's emotions, (2) emotion regulation, and (3) emotion-related parenting practices.

Method

Participants were parents of children aged 4-10 years from the Child and Parent Emotion Study (N = 869) (https://bmjopen.bmj.com/content/10/10/e038124). Parents completed self-reported measures of emotion socialization processes via an online survey, which took 20-30 min to complete. Data included in the current study were collected May-August 2019. We conducted a latent profile analysis of parents' emotion socialization (13 indicators). To assess reliability of the profiles, we examined stability of the profiles across (1) parents of children in early versus middle childhood, and (2) fathers versus mothers, via measurement invariance testing. Further, to assess for construct validity of the profiles, we examined concurrent associations between six criterion constructs and parents' emotion socialization profiles.

Results

A three-profile model emerged characterizing parents by: (1) emotion coaching; (2) emotion dismissing; (3) emotion disengaged. There was strong support for construct validity and reliability.

Discussion

Our study provides empirical support for distinct differentiated classifications of emotion coaching and emotion dismissing parenting, aligned with emotion socialization theories. We further extend on extant theory and suggest a third 'emotion disengaged' classification, describing parents with moderate levels of emotion dismissing parenting and low levels of emotion coaching parenting. It should be noted that the profiles were derived with self-report data, therefore, data may have been biased by contextual factors. Furthermore, the study sample consisted of Western families from affluent backgrounds. The field should focus efforts on conducting person-centered studies with more diverse samples in future.

Differences in mental health and alcohol use across profiles of COVID-19 disruptions

This study aimed to examine differences in mental health and alcohol use outcomes across distinct patterns of work, home, and social life disruptions associated with the COVID-19 pandemic. Data from 2093 adult participants were collected from September 2020 to April 2021 as a part of a larger study examining the impacts of the COVID-19 pandemic on substance use. Participants provided data on COVID-19 pandemic experiences, mental health outcomes, media consumption, and alcohol use at baseline. Alcohol use difficulties, including problems related to the use, desire to use alcohol, failure to cut down on alcohol use, and family/friend concern with alcohol use, were measured at 60-day follow-up. Factor mixture modeling followed by group comparisons, multiple linear regressions, and multiple logistic regressions was conducted. A four-profile model was selected. Results indicated that profile membership predicted differences in mental health and alcohol use outcomes above and beyond demographics. Individuals experiencing the most disruption reported the strongest daily impact of COVID-19 and significantly high levels of depression, anxiety, loneliness, overwhelm, alcohol use at baseline, and alcohol use difficulties measured at 60-day follow-up. The findings highlight the need for integrated mental health and/or alcohol services and social services targeting work, home, and social life during public health emergencies in order to respond effectively and comprehensively to the needs of those requiring different types of support.

"I Ran to Make a Point": Predicting and Preventing Youth Runaway from Foster Care

Youth who run away from foster care experience danger to health and safety and increased risk of adverse child welfare outcomes. By applying a concurrent mixed-methods approach, this study aimed to develop a deeper understanding of runaway risk that used a person-centered lens and amplified youth voices. Collectively, this approach can inform service innovations to support youth placed in out-of-home care. Working with a foster care agency in Kansas, data sources comprised administrative data for youth ages 12 + in care, and interview data with 20 youth, 12 + in care. Quantitative analyses involved latent class analysis followed by multinomial logistic regression to investigate whether the population of youth in care was comprised of subpopulations with differential runaway risk and whether subpopulations would predict runaway behaviors. Qualitative analyses applied modified analytic inductive thematic analysis to explore critical life experiences that may act as risk or protective factors of running away from care. Results revealed four sub-populations which were characterized by their previous family and system experiences. Additionally, class membership, gender, number of siblings, and age were statistically significant predictors of runway behaviors. Youth interviews revealed five key themes on life experiences that mitigate or exacerbate youths' runaway behaviors. Recommendations resulting from this study were provided in three key areas: (1) improving family visitation and maintaining youth connections with self-identified family and non-relative kin; (2) supporting service approaches for youth that honor and amplify their voices, choices, and family connections; and (3) improving placement quality and individualization of services.

Targeting COVID-19 vaccine-hesitancy in college students: An audience-centered approach

Objective: The study tested potential factors that differentiated the COVID-19 vaccine-hesitant and -inclined college students and, based on these factors, identified subgroups of the vaccine-hesitant students. Participants: Participants were 1,183 U.S. college students attending four-year universities or community colleges recruited through Qualtrics between January 25 and March 3, 2021. Methods: Participants completed an online survey assessing their COVID-19 vaccination intention, perceived risks of COVID-19 and the COVID-19 vaccines, efficacy beliefs regarding COVID-19 and the COVID-19 vaccines, and emotions toward taking the COVID-19 vaccines. Results: Vaccine-hesitant and -inclined college students varied in their emotions, risk perceptions, and efficacy beliefs regarding the virus and the vaccines. Using these factors as indicators, vaccine-hesitant college students were classified into five latent subgroups with distinct characteristics. Conclusions: In identifying subgroups of the vaccine-hesitant college students, the study has important insights to offer regarding the design of vaccine-promotion messaging strategies targeting the college student population.

Associations between self-reported sleep patterns and health, cognition and amyloid measures: results from the Wisconsin Registry for Alzheimer's Prevention

Previous studies suggest associations between self-reported sleep problems and poorer health, cognition, Alzheimer's disease pathology and dementia-related outcomes. It is important to develop a deeper understanding of the relationship between these complications and sleep disturbance, a modifiable risk factor, in late midlife, a time when Alzheimer's disease pathology may be accruing. The objectives of this study included application of unsupervised machine learning procedures to identify distinct subgroups of persons with problematic sleep and the association of these subgroups with concurrent measures of mental and physical health, cognition and PET-identified amyloid. Dementia-free participants from the Wisconsin Registry for Alzheimer's Prevention (n = 619) completed sleep questionnaires including the Insomnia Severity Index, Epworth Sleepiness Scale and Medical Outcomes Study Sleep Scale. K-means clustering analysis identified discrete sleep problem groups who were then compared across concurrent health outcomes (e.g. depression, self-rated health and insulin resistance), cognitive composite indices including episodic memory and executive function and, in a subset, Pittsburgh Compound B PET imaging to assess amyloid burden. Significant omnibus tests (P < 0.05) were followed with pairwise comparisons. Mean (SD) sample baseline sleep assessment age was 62.6 (6.7). Cluster analysis identified three groups: healthy sleepers [n = 262 (42.3%)], intermediate sleepers [n = 229 (37.0%)] and poor sleepers [n = 128 (20.7%)]. All omnibus tests comparing demographics and health measures across sleep groups were significant except for age, sex and apolipoprotein E e4 carriers; the poor sleepers group was worse than one or both of the other groups on all other measures, including measures of depression, self-reported health and memory complaints. The poor sleepers group had higher average body mass index, waist-hip ratio and homeostatic model assessment of insulin resistance. After adjusting for covariates, the poor sleepers group also performed worse on all concurrent cognitive composites except working memory. There were no differences between sleep groups on PET-based measures of amyloid. Sensitivity analyses indicated that while different clustering approaches resulted in different group assignments for some (predominantly the intermediate group), between-group patterns in outcomes were consistent. In conclusion, distinct sleep characteristics groups were identified with a sizable minority (20.7%) exhibiting poor sleep characteristics, and this group also exhibited the poorest concurrent mental and physical health and cognition, indicating substantial multi-morbidity; sleep group was not associated with amyloid PET estimates. Precision-based management of sleep and related factors may provide an opportunity for early intervention that could serve to delay or prevent clinical impairment.

Who's at greatest risk? Latent profiles of alcohol and cannabis use and related consequences among college students

BACKGROUND

There is significant heterogeneity in alcohol and cannabis use patterns among college students, with some engaging in use patterns that heighten their risk for adverse consequences. Person-centered approaches can help identify those subgroups of students with riskier use patterns. Latent Profile Analyses (LPA) were conducted to identify subgroups based on alcohol and cannabis use frequency and quantity, to explore demographic covariates and to examine mean differences across subgroups on alcohol- and cannabis-related consequences, simultaneous use, and other substance use.

METHODS

Participants were 2,423 college students (Mage = 20.1; 72  % female) recruited from seven US universities who endorsed past-month alcohol and cannabis use and completed an online survey of substance use behaviors.

RESULTS

A four-profile solution was the best fitting model. Profile 1 represented "light, infrequent alcohol and cannabis use" (73.8 %), Profile 2 represented "heavy, infrequent alcohol and moderate, frequent cannabis use" (15.9 %), Profile 3 represented "moderate, frequent alcohol and cannabis use" (5.6 %) and Profile 4 represented "very heavy, frequent alcohol and heavy, frequent cannabis use" (4.7 %). Students who identify as male, White non-Hispanic, and/or Greek-affiliated were more likely to be in the heavy alcohol use profiles. Profiles 3 and 4 represent high-risk profiles, with both having a higher likelihood of simultaneous use, Profile 3 endorsing more cannabis consequences, and Profile 4 endorsing more alcohol consequences.

CONCLUSION

Results suggest that heavy alcohol or heavy co-use heightens risk for serious adverse consequences.

Growth in basic science knowledge in first-year medical school and USMLE Step 1 results: a longitudinal investigation at one school

PURPOSE

Our US medical school uses National Board of Medical Examiners (NBME) tests as progress tests during the pre-clerkship curriculum to assess students. In this study, we examined students' growth patterns using progress tests in the first year of medical school to identify students at risk for failing United States Medical Licensing Examination (USMLE) Step 1.

METHOD

Growth Mixture Modeling (GMM) was used to examine the growth trajectories based on NBME progress test scores in the first year of medical school. Achieving a passing score on the USMLE Step 1 at the end of the second year of medical school was used as the distal outcome, controlling for Medical College Admissions Test (MCAT) scores and underrepresented in medicine (URiM) status.

RESULTS

A total of 518 students from a US medical school were included in the analysis. Five different growth patterns were identified based on students' NBME test results. Seventy-eight students identified in Group 1 had the lowest starting NBME test score (mean = 33.6, 95% CI 32.0-35.2) and lowest growth rate (mean = 2.30, 95% CI 2.06-2.53). All 26 students who failed Step 1 at the end of the second year were in Group 1 (failing rate = 33%). Meanwhile Group 4 (n = 65 students) had moderate starting NBME test scores (mean = 37.9, 95% CI 36.3-39.0) but the highest growth rate with mean slope at 6.07 (95% CI 5.40-6.73). This group of students achieved significant higher USMLE Step1 scores comparing with the 3 other groups of students (P < 0.05).

CONCLUSIONS

Our study found students had heterogeneous growth patterns in progress test results in their first year of medical school. Growth patterns were highly predictive of USMLE step 1 results. This study can provide performance benchmarks for our future students to assess their progress and for medical educators to identify students who need support and guidance.

Patterns of Scientific Reasoning Skills among Pre-Service Science Teachers: A Latent Class Analysis

We investigated the scientific reasoning competencies of pre-service science teachers (PSTs) using a multiple-choice assessment. This assessment targeted seven reasoning skills commonly associated with scientific investigation and scientific modeling. The sample consisted of 112 PSTs enrolled in a secondary teacher education program. A latent class (LC) analysis was conducted to evaluate if there are subgroups with distinct patterns of reasoning skills. The analysis revealed two subgroups, where LC1 (73% of the PSTs) had a statistically higher probability of solving reasoning tasks than LC2. Specific patterns of reasoning emerged within each subgroup. Within LC1, tasks involving analyzing data and drawing conclusions were answered correctly more often than tasks involving formulating research questions and generating hypotheses. Related to modeling, tasks on testing models were solved more often than those requiring judgment on the purpose of models. This study illustrates the benefits of applying person-centered statistical analyses, such as LC analysis, to identify subgroups with distinct patterns of scientific reasoning skills in a larger sample. The findings also suggest that highlighting specific skills in teacher education, such as: formulating research questions, generating hypotheses, and judging the purposes of models, would better enhance the full complement of PSTs’ scientific reasoning competencies.

COVID-19 Emergency Remote Teaching Opinions and Academic Performance of Undergraduate Students: Analysis of 4 Students’ Profiles. A Case Study

Distance learning due to the COVID-19 lockdown, commonly called emergency remote teaching (ERT), substantially changed the methodology of teaching and possibly students’ perceptions of the quality of lectures. Students’ opinions should be collected and analyzed jointly with other data such as academic performance to assess the effect of this pandemic on learning. A 20-question, 4-point Likert scale specific questionnaire was designed and validated twice by a panel of experts. The survey was sent to the 365 industrial engineering undergraduate students enrolled in a chemistry course. Responses (n = 233) and academic data were collected, and four student profiles were identified by using the k-means cluster analysis technique: ‘The Lucky’, ‘The Passive’, ‘The Autonomous Learner’ and ‘The Harmed’. Students experienced the ERT differently according to their profile. Undergraduates who were better autonomous learners excelled in academic performance and were more participative in the survey. In general, students preferred face-to-face classes over distance learning. Undergraduates’ learning has been impaired due to the circumstances. However, contrary to their beliefs, the situation has benefited them with respect to grades when comparing their performance with students from previous years. Discovering what challenges students faced to adapt to the situation is key to giving students tools to grow as autonomous learners and to enable educators to apply tailored teaching techniques to improve the quality of lectures and enhance student satisfaction.

Changes and Adaptations: How University Students Self-Regulate Their Online Learning During the COVID-19 Pandemic

During the COVID-19 (coronavirus disease 2019) pandemic, universities had to shift from face-to-face to emergency remote education. Students were forced to study online, with limited access to facilities and less contact with peers and teachers, while at the same time being exposed to more autonomy. This study examined how students adapted to emergency remote learning, specifically focusing on students' resource-management strategies using an individual differences approach. One thousand eight hundred university students completed a questionnaire on their resource-management strategies and indicators of (un)successful adaptation to emergency remote learning. On average, students reported being less able to regulate their attention, effort, and time and less motivated compared to the situation before the crisis started; they also reported investing more time and effort in their self-study. Using a k-means cluster analysis, we identified four adaptation profiles and labeled them according to the reported changes in their resource-management strategies: the overwhelmed, the surrenderers, the maintainers, and the adapters. Both the overwhelmed and surrenderers appeared to be less able to regulate their effort, attention, and time and reported to be less motivated to study than before the crisis. In contrast, the adapters appreciated the increased level of autonomy and were better able to self-regulate their learning. The resource-management strategies of the maintainers remained relatively stable. Students' responses to open-answer questions on their educational experience, coded using a thematic analysis, were consistent with the quantitative profiles. Implications about how to support students in adapting to online learning are discussed.

Burnout and engagement among PhD students in medicine: the BEeP study

INTRODUCTION

Using a self-determination theory framework, we investigated burnout and engagement among PhD students in medicine, and their association with motivation, work-life balance and satisfaction or frustration of their basic psychological needs.

METHOD

This cross-sectional study was conducted among PhD students at a university medical centre (n = 990) using an electronic survey on background characteristics and validated burnout, engagement, motivation and basic psychological needs questionnaires. Cluster analysis was performed on the burnout subscale scores to find subgroups within the sample which had similar profiles on burnout. Structural equation modelling was conducted on a hypothesized model of frustration of basic psychological needs and burnout.

RESULTS

The response rate was 47% (n = 464). We found three clusters/subgroups which were composed of PhD students with similar burnout profiles within the cluster and different profiles between the clusters. Cluster 1 (n = 199, 47%) had low scores on burnout. Clusters 2 (n = 168, 40%) and 3 (n = 55, 13%) had moderate and high burnout scores, respectively, and were associated with low engagement scores. Cluster 3, with the highest burnout scores, was associated with the lowest motivational, engagement, needs satisfaction and work-life balance scores. We found a good fit for the "basic psychological needs frustration associated with burnout" model.

DISCUSSION

The most important variables for burnout among PhD students in medicine were lack of sleep and frustration of the basic psychological needs of autonomy, competence and relatedness. These add to the factors found in the literature.