Medical School Strategies to Address Student Well-Being: A National Survey
Bibliographic record
Abstract
PURPOSE: To describe the breadth of strategies U.S. medical schools use to promote medical student well-being. METHOD: In October 2016, 32 U.S. medical schools were surveyed about their student well-being initiatives, resources, and infrastructure; grading in preclinical courses; and learning communities. RESULTS: Twenty-seven schools (84%) responded. Sixteen (59%) had a student well-being curriculum, with content scheduled during regular curricular hours at most (13/16; 81%). These sessions were held at least monthly (12/16; 75%), and there was a combination of optional and mandatory attendance (9/16; 56%). Most responding schools offered a variety of emotional/spiritual, physical, financial, and social well-being activities. Nearly one-quarter had a specific well-being competency (6/27; 22%). Most schools relied on participation rates (26/27; 96%) and student satisfaction (22/27; 81%) to evaluate effectiveness. Sixteen (59%) assessed student well-being from survey data, and 7 (26%) offered students access to self-assessment tools. Other common elements included an individual dedicated to overseeing student well-being (22/27; 82%), a student well-being committee (22/27; 82%), pass/fail grading in preclinical courses (20/27; 74%), and the presence of learning communities (22/27; 81%). CONCLUSIONS: Schools have implemented a broad range of well-being curricula and activities intended to promote self-care, reduce stress, and build social support for medical students, with variable resources, infrastructure, and evaluation. Implementing dedicated well-being competencies and rigorously evaluating their impact would help ensure appropriate allocation of time and resources and determine if well-being strategies are making a difference. Strengthening evaluation is an important next step in alleviating learner distress and ultimately improving student well-being.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".