Barriers to Accessing Mental Health Support Services in Undergraduate Medical Training: A Multicenter, Qualitative Study
Bibliographic record
Abstract
PURPOSE: Medical students report higher levels of burnout, anxiety, and depression compared with age-matched peers. These mental health challenges have been linked to reduced workplace productivity, empathy, and professionalism. Yet, students experiencing mental health issues often decide not to access mental health resources, citing limited time and concerns about confidentiality, stigma, and the cost of private therapy. This study aimed to provide a framework for understanding barriers medical students face regarding access to mental health resources. METHOD: A constructivist grounded theory approach was employed, with 24 students from 6 medical schools in Ontario, Canada, participating in semistructured telephone interviews between May 2019 and February 2020. Participants were purposively sampled to capture a broad range of experiences, institutional contexts, and training levels. The authors then developed a framework to conceptualize the barriers that medical students face while accessing mental health resources. RESULTS: The information obtained from the interviews revealed that the barriers were both overt and covert. Overt barriers were primarily administrative challenges, including restrictive leave of absence policies and sick days, mandatory reporting of extended sick leave time during the residency selection process, time-restricted academic and clinical schedules, and difficulty in accessing mental health supports during distance education. Covert barriers to accessing mental health supports included a medical culture not conducive to mental health, felt stigma (i.e., fear of stigma and being labeled as weak), and the hidden curriculum (i.e., the unofficial or unintended rules and mannerisms propagated within medical education systems). CONCLUSIONS: Better understanding the overt and covert barriers that medical students to face while accessing mental health supports may help guide and inspire new advocacy efforts to enhance medical 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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".