Signaling hostility: The relationship between witnessing weight‐based discrimination in medical school and medical student well‐being
Bibliographic record
Abstract
Abstract Environments that are hostile to one or more marginalized groups are known to have a negative effect on the mental health and well‐being of both targets and observers. Anti‐fat attitudes have been well documented in medical education, including the use of derogatory humor and discriminatory treatment toward higher‐weight patients. However, to date, it is not known what effect observing weight stigma and discrimination during medical school has on medical students’ psychological health and wellbeing, sense of belonging, and medical school burnout. The present study surveyed a total of 3994 students enrolled across 49 US medical schools at the start of their first year and at the end of their fourth year. Participants reported the frequency with which they had observed stigmatizing and discriminatory behaviors targeted at both higher‐weight patients and higher‐weight students during their four years of medical school. Observed weight stigma was prevalent, and was associated with worse psychological and general health, reduced medical school belonging and increased medical school burnout. The indirect effects of observed weight stigma on medical school burnout, via belonging, psychological health, and general health, were statistically significant in the sample as a whole, but were more pronounced in higher‐weight students. This effect may be explained, in part, by the relationship between observed stigma and medical school belonging. Higher levels of observed stigma were associated with reduced feelings of belonging in higher‐weight but not normative‐weight students. Top‐down institutional culture change is needed to rectify this situation, which is detrimental to both students and patients.
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".