We Burn Out, We Break, We Die: Medical Schools Must Change Their Culture to Preserve Medical Student Mental Health
Bibliographic record
Abstract
The author explores medical student depression and suicide through the lens of the author's personal struggle during the first 2 years of medical school. While the author's story is unique, other medical students have also faced challenges that have led them to consider a permanent solution to a temporary problem. Although resources are available, stigma represents a significant barrier for students as they decide whether to seek help. Students fear that showing the slightest hint of vulnerability or imperfection will be used against them in an advancement committee, a course evaluation, or the dean's letter for residency applications. This difficulty asking for help and the subsequent suppression of feelings can lead to burnout and ultimately to increased risk of suicide. The author calls for medical schools to make changes to their culture to preserve medical student mental health. These include committing to helping students who are struggling academically or psychologically, implementing an institution-wide program to screen for individuals at risk for suicide, educating members of the institution's community about depression to destigmatize seeking help for mental health, and ensuring confidential mental health services are readily available to those who need them. But, most importantly, medical schools must create a culture that normalizes the need for self-care and includes vulnerability as part of training in professionalism.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.031 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.007 | 0.019 |
| 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".