Canadian Physicians’ Attitudes towards Accessing Mental Health Resources
Bibliographic record
Abstract
Despite their rigorous training, studies have shown that physicians experience higher rates of mental illness, substance abuse, and suicide compared to the general population. An online questionnaire was sent to a random sample of physicians across Canada to assess physicians' knowledge of the incidence of mental illness among physicians and their attitudes towards disclosure and treatment in a hypothetical situation where one developed a mental illness. We received 139 responses reflecting mostly primary care physicians and nonsurgical specialists. The majority of respondents underestimated the incidence of mental illness in physicians. The most important factors influencing respondent's will to disclose their illness included career implications, professional integrity, and social stigma. Preference for selecting mental health treatment services, as either outpatients or inpatients, was mostly influenced by quality of care and confidentiality, with lower importance of convenience and social stigma. Results from this study suggest that the attitudes of physicians towards becoming mentally ill are complex and may be affected by the individual's previous diagnosis of mental illness and the presence of a family member with a history of mental illness. Other factors include the individual's medical specialty and level of experience. As mental illness is common among physicians, one must be conscious of these when offering treatment options.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".