Medical Student Beliefs about Disclosure of Mental Health Issues: A Qualitative Study
Bibliographic record
Abstract
In 2012 the United Kingdom's General Medical Council (GMC) commissioned research to develop guidance for medical schools on how best to support students with mental illness. One of the key findings from medical student focus groups in the study was students' strong belief that medical schools excluded students on mental health grounds. Students believed mental illness was a fitness to practice matter that led to eventual dismissal, although neither personal experience nor empirical evidence supported this belief. The objective of the present study was a deeper exploration of this belief and its underlying social mechanisms. This included any other beliefs that influenced medical students' reluctance to disclose a mental health problem, the factors that reinforced these beliefs, and the feared consequences of revealing a mental illness. The study involved a secondary analysis of qualitative data from seven focus groups involving 40 student participants across five UK medical schools in England, Scotland, and Wales. Student beliefs clustered around (1) the unacceptability of mental illness in medicine, (2) punitive medical school support systems, and (3) the view that becoming a doctor is the only successful career outcome. Reinforcing mechanisms included pressure from senior clinicians, a culture of "presenteeism," distrust of medical school staff, and expectations about conduct. Feared consequences centered on regulatory "fitness to practice" proceedings that would lead to expulsion, reputational damage, and failure to meet parents' expectations. The study's findings provide useful information for veterinary medical educators interested in creating a culture that encourages the disclosure of mental illness and contributes to the debate about mental illness within the veterinary profession.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".