Understanding PTSD among correctional workers in Manitoba, Canada: Key considerations of social variables
Bibliographic record
Abstract
Abstract Mounting evidence highlights the high prevalence of posttraumatic stress disorder (PTSD) among correctional workers. The current analysis draws on survey response data to present a social profile of correctional workers in the province of Manitoba (n = 580), Canada, who screened positive for PTSD (n = 196). We examined demographic information, professional history information, and adverse work exposure experiences, as well as treatment and support patterns. The analysis was not intended to identify correlates of PTSD development among correctional workers, but did identify the characteristics, professional and personal situations, and treatment experiences of correctional workers who screened positive for PTSD. The results highlight the multidimensional nature of work stressors, the pronounced problem of work–life conflict, and variations in seeking supports and treatments. Generally, participants screening positive for PTSD reported higher exposure to potentially psychologically traumatic events, higher environmental or occupational stressors at work, and many had prior work experience as public safety personnel. Correctional workers who screened positive for PTSD appeared more likely to access mental health supports. Promoting proactive support seeking for mental health treatment may help to mitigate the severity, frequency, stigma, and length of mental health challenges among 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".