Barriers Faced by Medical Students in Seeking Mental Healthcare: A Scoping Review
Bibliographic record
Abstract
Abstract Introduction Medical students commonly encounter psychological illness in both the classroom and in clinical settings, but unfortunately also face a variety of mental health issues themselves. Despite the availability of professional help on medical campuses, help-seeking continues to be a challenge for some students (Rotenstein et al., 2016). Our review aimed to identify the barriers medical students face when seeking professional mental healthcare. Question What are the barriers to seeking professional mental healthcare faced by medical students? Methods A MeSH search was created for articles using the databases PubMed, Embase, and PsychINFO to identify articles specifically about medical students and their barriers to professional mental healthcare. 454 articles were identified and screened by title/abstract and then full text. Data was extracted from 33 articles using an independent framework. Barriers identified were compiled and reported. Results From a total of 33 articles, the most commonly identified barriers to seeking mental healthcare were fear of negative effect on residency/career opportunities, fear of confidentiality breach, stigma and fear of shaming from peers, lack of perceived seriousness/normalization of symptoms, lack of time, and fear of documentation on academic record. Students also preferred to seek care outside of their institution from fear of provider being an academic preceptor. Discussion Many of the barriers to mental healthcare faced by medical students relate to a fear of academic/career reprisal and fear of confidentiality breach. It appears that despite recent efforts to decrease stigma surrounding mental illness, many medical students struggle to seek appropriate support. Access to mental health care can be improved by increasing transparency regarding what information will be displayed on academic records, dispelling common myths about mental healthcare and increasing awareness about available resources for medical students.
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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 | 0.011 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".