Exposures to correctional-specific potentially psychologically traumatic events among Ontario provincial correctional workers.
Bibliographic record
Abstract
OBJECTIVE: = 980; 50.7% female) and estimated associations with mental health symptoms. METHOD: The survey data used are from the Provincial Ontario Correctional Worker Mental Health Prevalence Study in Canada. Cross-tabulations, Chi-square tests, ANOVAs, and logistic regression are used to examine the following: (a) the distribution of correctional-specific PPTEs across correctional worker occupational categories; (b) the frequencies of correctional-specific PPTE exposures; and (c) the association between correctional-specific PPTEs and mental disorders. Population-attributable fractions (PAFs) are used to estimate the proportion of mental disorders that may be attributable to PPTE exposures. RESULTS: = 3.33). There were statistically significant differences in PPTE exposure patterns across correctional worker categories. PPTEs were positively associated with mental disorder symptoms for all participants. PAFs indicated that mental disorders among correctional workers could reduce by 66%-80% with the elimination of all PPTEs among correctional workers. CONCLUSIONS: Eliminating PPTE exposures is unlikely in the correctional environment; nevertheless, the results indicate that mitigating PPTEs may drastically improve the mental health of correctional workers. (PsycInfo Database Record (c) 2023 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".