Extracurricular Humanism in Medicine Initiative and Medical Student Wellness: Retrospective Study
Bibliographic record
Abstract
Background Humanism in Medicine Initiative (HIMI), an extracurricular program at Ohio State University College of Medicine (OSUCOM) with 27 subgroups, fosters the humanities. Stress and burnout among first- and second-year medical students are prevalent across the United States. Solutions for stress among first- and second-year medical students have been proposed, but no gold standard exists. The relationship of humanism with stress and burnout has yet to be described in the literature. Objective This study investigates the relationship between participation in the HIMI and stress, burnout, and academic success among first- and second-year medical students. Methods First- and second-year medical students enrolled at OSUCOM between August 2018 and August 2019 were recruited. Attendance in the HIMI and membership records were used to measure their participation. Curricular examination scores and those on Step 1 of United States Medical Licensing Examination (USMLE) were used to measure academic success. Stress and burnout were measured using the Maslach Burnout Inventory and the Perceived Stress Scale. Results In total, 412 students were enrolled with 362 (87%) students participating in HIMI. Those with high participation were more often Black, Asian, female, or with a humanities undergraduate major compared to the overall study population. There were significant relationships between Gold Humanism Honors Society (GHHS) induction and participation of first- and second-year medical students in service- (χ21=5.8, P<.05) or leadership-focused (χ21=19.3, P<.001) HIMI groups. Medium levels of participation in the HIMI were associated with significantly higher stress. Performance on the Step 1 USMLE was not significantly associated with participation levels in the HIMI (low=233.7 vs high=238.0; P=.10). Conclusions The HIMI is an extracurricular program vastly utilized by first- and second-year medical students at OSUCOM and did not impact Step 1 USMLE scores. Medium participation in the HIMI was associated with higher stress, and service- and leadership-focused HIMI participation was associated with a higher level of induction to the GHHS. This study identifies areas for future studies to understand the relationship of the HIMI with stress and academic success.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".