Mental Disorder Symptoms Among Correctional Workers In Canada
Bibliographic record
Abstract
Background: Correctional workers are regularly exposed to potentially traumatic events (PTEs). Such exposures increase risk for mental disorders involving substantial personal and social costs. Unfortunately, available data on exposure to PTEs and associations with mental disorders in Canadian correctional workers remains sparse. The current research was designed to provide estimates of the frequencies of PTE exposure within Federal correctional workers and symptoms of mental health disorders.Methods: The data for the current study were collected as part of a larger study, using a web-based self-report survey made available to participants in English or French. The survey included established self-report measures for exposures to PTEs and mental disorder symptoms.Results: 1308 Federal correctional workers (43.3% male) from across Canada responded and reported exposures to 16 PTE types (M=9.88, SD=3.88). 88.7% reported being exposed to physical assault, 85.6% to sudden violent death, 80.6% to sudden accidental deaths, and 78.8% to assault with a weapon. When asked to identify their worst traumatic experience, 24.0% reported exposure to sudden violent death, while u2018physical assaultu2019 was ranked highest by 13.0%. There were statistically significant relationships between PTE exposures and operational stress injuries (OSIs) such as PTSD, Depression, Anxiety Disorders, Disorder, and Alcohol Use Disorder.Discussion: For Federal correctional workers, the most disturbing PTE exposures were related to violent death and physical assault. Given PTE exposure prevalence, and the association with OSIs, policy makers should ensure evidence-based mental health resources are readily available for correctional workers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.003 | 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".