Canadian Psychiatrists' Current Attitudes, Practices, and Knowledge regarding Fitness to Drive in Individuals with Mental Illness: A Cross-Canada Survey
Bibliographic record
Abstract
OBJECTIVES: To assess current attitudes, practices, and knowledge of Canadian psychiatrists regarding fitness to drive in individuals with mental illness and to explore variations according to provincial legislation. METHOD: We carried out a national cross-sectional survey, using a random sample of psychiatrists. We used a mail survey to collect data. RESULTS: In total, 248 psychiatrists participated; the response rate was 54.2% on traced subjects. The majority (64.1%) reported that they strongly agreed or agreed that addressing patients' fitness to drive is an important issue. However, only 18.0% of respondents were always aware of whether their patients were active drivers. One-fourth strongly agreed or agreed that they were confident in their ability to evaluate fitness to drive. In discretionary provinces, 29.3% of psychiatrists reported not knowing their provincial legislation, as did 14.6% of psychiatrists in mandatory provinces; of those responding, 54.0% from discretionary provinces and 2.8% from mandatory provinces gave incorrect answers. DISCUSSION: Psychiatrists' responses demonstrate a broad range of attitudes, practices, and knowledge. There appears to be a large gap between what is expected of psychiatrists and their readiness and self-perceived ability to make informed clinical decisions related to driving safety. CONCLUSION: There is a clear need for education and guidelines to assist psychiatrists in decision making about driving fitness.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".