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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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; both teacher heads agree on what is shown here.
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".