Suicidal Behaviors Among Nurses in Canada
Bibliographic record
Abstract
BACKGROUND: Nurses are regularly exposed to potentially psychologically traumatic events, experience high rates of burnout, and may be at an elevated risk of death by suicide. Few studies have assessed for suicidal behaviors among Canadian nurses, and factors that may increase risk for suicidal behaviors are unknown. PURPOSE: The current study was designed to assess past-year and lifetime suicidal behavior (i.e., ideation, plans, and attempts) using a large sample of Canadian nurses. METHOD: 3969; 94.3% women) completed an online survey including measures of suicidal behavior and symptoms of mental disorders. RESULTS: Considerable proportions of participants reported past-year and/or lifetime suicidal ideation (10.5%, 33.0%), plans (4.6%, 17.0%), and attempts (0.7%, 8.0%), considerably higher than general population estimates. Significant differences were identified across age groups, years of service, marital status, regional location, and nursing type (e.g., registered psychiatric nurses, licensed practical nurses, registered nurses). Participants who screened positive for almost all measured mental disorders had significantly higher rates of suicidal behavior. CONCLUSIONS: The results necessitate further research to evaluate risk factors contributing to suicidal behavior in Canadian nurses and methods to decrease the risk (e.g., developing effective monitoring and prevention measures).
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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 | 0.003 |
| 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) | 0.002 | 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".