Occupational Stressors and Mental Health Disorders: A National Study of Correctional Service Providers in Canada's Provincial and Territorial Systems
Bibliographic record
Abstract
Correctional workers (CWs) experience organizational (e.g., staff shortages, administrative burdens) and operational stressors (e.g., exposure to potentially psychologically traumatic events [PPTEs]) when completing their occupational responsibilities. In the current Canadian study, we assessed the average stress levels for diverse organizational and operational stressors among CWs across occupational groups (e.g., institutional operational, correctional officers, community operations, management, and administrators), provincial and territorial jurisdictions, and pre versus during COVID-19. We examined the relationships between 40 work-related stressors, including PPTE exposures and prevalence of positive screens for several mental health disorders (e.g., posttraumatic stress disorder, major depressive disorder, general anxiety disorder, panic disorder, alcohol use disorder). Results further evidence organizational and operational stressors beyond PPTE as being correlates of mental health challenges among CWs. Reducing organizational stress by increasing staffing and leadership training, improving communication and access to specialized treatment resources, mitigating PPTE exposures, and supporting collegial relationships may all potentiate improvements for the mental health of CWs.
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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 | 0.002 | 0.005 |
| 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) | 0.001 | 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".