Provincial Correctional Service Workers: The Prevalence of Mental Disorders
Bibliographic record
Abstract
Correctional service employees in Ontario, Canada (n = 1487) began an online survey available from 2017 to 2018 designed to assess the prevalence and correlates of mental health challenges. Participants who provided data for the current study (n = 1032) included provincial staff working in institutional wellness (e.g., nurses) (n = 71), training (e.g., program officers) (n = 26), governance (e.g., superintendents) (n = 82), correctional officers (n = 553), administration (e.g., record keeping) (n = 25), and probation officers (n = 144, parole officers). Correctional officers, workers in institutional administration and governance positions, and probation officers reported elevated risk for mental disorders, most notably posttraumatic stress disorder (PTSD) and major depressive disorder. Among institutional correctional staff, 61.0% of governance employees, 59.0% of correctional officers, 43.7% of wellness staff, 50.0% of training staff, and 52.0% of administrative staff screened positive for one or more mental disorders. In addition, 63.2% of probation officers screened positive for one or more mental disorders. Women working as correctional officers were more likely to screen positive than men (p < 0.05). Across all correctional occupational categories positive screens for each disorder were: 30.7% for PTSD, 37.0% for major depressive disorder, 30.5% for generalized anxiety disorder, and 58.2% for one or more mental disorders. Participants between ages 40 and 49 years, working in institutional governance, as an institutional correctional officer, or as a probational officer, separated or divorced, were all factors associated (p < 0.05) with screening positive for one or more mental disorders. The prevalence of mental health challenges for provincial correctional workers appears to be higher than federal correctional workers in Canada and further supports the need for evidence-based mental health solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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) | 0.003 | 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".