Mental Health of Canadian Firefighters: The Impact of Sleep
Bibliographic record
Abstract
Volunteer and career firefighters are at risk of major depressive disorders, posttraumatic stress disorder (PTSD), alcohol use disorder, and other mental health disorders due to the demanding and unpredictable nature of their employment. The mental health risks are exacerbated by the need to work extended hours, night shifts, and/or rotating schedules, or the competing demands of other employment, especially in volunteer firefighters. The mental health disorders and risk factors interact with altered sleeping patterns. In the current study, we examined volunteer and career firefighters regarding the association between mental health and sleep, drawing from a national Canadian mental health survey of 1217 firefighters. Most (69%) of the firefighters reported less than ideal sleep quality and 21% screened positive for clinical insomnia, with no significant difference between volunteer and career subgroups. Firefighters with insomnia had higher odds ratios (OR) and frequencies for PTSD (OR = 4.98), generalized anxiety disorder (OR = 7.15), panic disorder (OR = 6.88), social phobia (OR = 4.98), and major depressive disorder (OR = 7.91), than firefighters without insomnia. The burden of sleep disorders and their association with mental health disorders suggests that sleep should be considered in health monitoring and self-management, environmental design, fire service work-organization policies, and health programming.
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 | 0.000 |
| 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".