Mental Disorder Symptoms Among Nurses in Canada
Bibliographic record
Abstract
BACKGROUND: Nurses face regular exposures to potentially psychologically traumatic events as part of their occupational responsibilities. Cumulative stress due to repeated exposure to such events is associated with poor mental health and an increased risk of developing clinically significant symptoms consistent with some mental disorders. PURPOSE: The current study was designed to estimate rates of mental disorder symptoms among nurses in Canada and identify demographic characteristics that are associated with increased risk for mental disorder symptoms. METHOD: An online survey was conducted with Canadian nurses in both English and French. Participants were recruited largely through the Canadian Federation of Nurses Unions (CFNU) member unions, non-CFNU member unions, and social media. The survey assessed current mental disorder symptoms using well-validated screening measures. RESULTS: A total of 4267 participants (93.8% women) completed the survey. Almost half of participants screened positive for a mental disorder (i.e., 47.9%). No gender differences emerged. Significant differences in proportions of positive screens based on each measure were found across demographic groups (e.g., age, province of residence, type of nurse). CONCLUSIONS: The rate of positive screens appears much higher than mental disorder prevalence rates in the general Canadian population, but there were important methodological differences. The current results provide potentially important information to support researchers and healthcare administrators to investigate possible ways to mitigate and manage mental health in nursing workplaces.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".