Sleep Quality and Mental Disorder Symptoms among Canadian Public Safety Personnel
Bibliographic record
Abstract
Poor sleep quality is associated with numerous mental health concerns and poorer overall physical health. Sleep disturbances are commonly reported by public safety personnel (PSP) and may contribute to the risk of developing mental disorders or exacerbate mental disorder symptoms. The current investigation was designed to provide estimates of sleep disturbances among PSP and explore the relationship between sleep quality and mental health status. PSP completed screening measures for sleep quality and diverse mental disorders through an online survey. Respondents (5813) were grouped into six categories: communications officials, correctional workers, firefighters, paramedics, police officers, and Royal Canadian Mounted Police (RCMP). Many PSP in each category reported symptoms consistent with clinical insomnia (49–60%). Rates of sleep disturbances differed among PSP categories (p < 0.001, ω = 0.08). Sleep quality was correlated with screening measures for post-traumatic stress disorder (PTSD), depression, anxiety, social anxiety disorder, panic disorder, and alcohol use disorder for all PSP categories (r = 0.18–0.70, p < 0.001). PSP who screened positive for insomnia were 3.43–6.96 times more likely to screen positive for a mental disorder. All PSP reported varying degrees of sleep quality, with the lowest disturbances found among firefighters and municipal/provincial police. Sleep appears to be a potentially important factor for PSP mental health.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".