Exposures to potentially traumatic events among public safety personnel in Canada.
Bibliographic record
Abstract
Canadian Public Safety Personnel (e.g., correctional workers, dispatchers, firefighters, paramedics, and police) are regularly exposed to potentially traumatic events, some of which are highlighted as critical incidents warranting additional resources. Unfortunately, available Canadian public safety personnel data measuring associations between potentially traumatic events and mental health remains sparse. The current research quantifies estimates for diverse event exposures within and between several categories of public safety personnel. Participants were 4,441 public safety personnel (31.7% women) in 1 of 6 categories (i.e., dispatchers, correctional workers, firefighters, municipal/provincial police, paramedics, and Royal Canadian Mounted Police). Participants reported exposures to diverse events including sudden violent (93.8%) or accidental deaths (93.7%), serious transportation accidents (93.2%), and physical assaults (90.6%), often 11+ times per event. There were significant relationships between potentially traumatic event exposures and all mental disorders. Sudden violent death and severe human suffering appeared particularly related to mental disorder symptoms, and therein potentially defensible as critical incidents. The current results offer initial evidence that (a) potentially traumatic event exposures are diverse and frequent among diverse Canadian public safety personnel; (b) many different types of exposure can be associated with mental disorders; (c) event exposures are associated with diverse mental disorders, including but not limited to posttraumatic stress disorder, and mental disorder screens would be substantially reduced in the absence of exposures; and (d) population attributable fractions indicated a substantial reduction in positive mental disorder screens (i.e., between 29.0 and 79.5%) if all traumatic event exposures were eliminated among Canadian public safety personnel.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 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".