Exposures to potentially psychologically traumatic events: A national study of correctional workers in Canadian provinces and territories.
Bibliographic record
Abstract
OBJECTIVE: = 3,740) in diverse occupational roles across all provinces and territories are exposed to different PPTEs. METHOD: Using robustly established self-report measures in surveys of each provincial and territorial correctional system, participants were grouped into consistent occupational categories to allow for comparisons and measurements of PPTE exposure and mental health disorder prevalence. RESULTS: = 4.24). The most prevalent PPTE exposure type was physical assault (88.5%), followed by exposure to a sudden violent death (81.1%), sudden accidental death (76.8%), and assault with a weapon (76.0%). The least prevalent PPTE exposure types were combat (25.5%); serious injury, harm, or death you caused to someone else (28.0%); and captivity (35.4%). Most PPTE exposures were related to statistically significantly increased odds of screening positive for various mental health disorders, evidencing correctional workers are frequently experiencing injurious psychological traumas associated with mental health disorder symptoms. CONCLUSIONS: We discuss our results in the context of correctional worker wellness and conclude with recommendations for policies to address mental health treatment and resource gaps in correctional services. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".