Attitudes of Canadian psychiatry residents if mentally ill: awareness, barriers to disclosure, and help-seeking preferences
Bibliographic record
Abstract
Background: The medical culture is defined by mental illness stigma, non-disclosure, and avoidance of professional treatment. Little research has explored attitudes and help-seeking behaviors of psychiatry trainees if they were to become mentally ill.Method: Psychiatry residents (n = 106) from training centres across Ontario, Canada completed a postal survey on their attitudes, barriers to disclosure, and help-seeking preferences in the context of hypothetically becoming mentally ill.Results: Thirty-three percent of respondents reported personal history of mental illness and the frequency of mental illness by year of training did not significantly differ. The most popular first contact for disclosure of mental illness was family and friends (n = 61, 57.5%). Frequent barriers to disclosure included career implications (n = 39, 36.8%), stigma (n = 11, 10.4%), and professional standing (n = 15, 14.2%). Personal history of mental illness was the only factor associated with in-patient treatment choice, with those with history opting for more formal advice versus informal advice.Conclusions: At the level of residency training, psychiatrists are reporting barriers to disclosure and help-seeking if they were to experience mental illness. A majority of psychiatry residents would only disclose to informal supports. Those with a history of mental illness would prefer formal treatment services over informal services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".