Utilizing Learning Communities to Implement a Wellness-in-Action Session in Undergraduate Medical Education
Bibliographic record
Abstract
OBJECTIVES: Learning communities (LCs) are on the rise in medical education. Key features of LCs include fostering student learning, wellness, and engagement. The ways in which different programs are currently using LCs are still relatively new, however, and literature on this topic is scarce. We developed and incorporated a wellness-in-action session, coined Brian's Field Day, into our LC curriculum with the aim to improve self-care, camaraderie, and socialization. METHODS: More than 180 first-year medical students and 20 LC faculty advisors participated in Brian's Field Day, a large-scale 1-hour wellness-in-action session, in academic years (AYs) 2021-2022 and 2022-2023. The session was divided into different activities, including yoga, meditation, dodgeball, kickball, coloring, pet therapy, board games, walking, improvisation, and Zumba. All students received an electronic survey assessing their perceptions of the activity. RESULTS: The overall response rate was 47% (86/183 students) in AY 2021-2022 and 49% (90/182 students) in AY 2022-2023. Overall, postactivity perceptions were positive with mean scores ranging from 4.00 to 4.61 (standard deviation [SD] range ± 0.69-1.30) for "enjoyment," "improved camaraderie," "should continue as part of the curriculum," and "would like more events like this." The mean was 3.96 (SD ±1.15) and 3.83 (SD ±1.38) for "improving self-care" in AY 2021-2022 and AY 2022-2023, respectively. Themed qualitative comments also indicated improved student-student and faculty-student bonding and the desire for more similar activities. CONCLUSIONS: Academic stress among medical students is high and many schools have incorporated LCs into their curricula to help with socialization and promotion of wellness and community. As more schools adopt LCs, they should consider integrating a wellness-in-action session as a fun way to augment both peer-to-peer and student-faculty relationships.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".