Longitudinal study of Canadian correctional workers' wellbeing, organizations, roles and knowledge (CCWORK): Baseline demographics and prevalence of mental health disorders
Bibliographic record
Abstract
Background: Researchers and practitioners have begun to recognize and empirically examine the mental health challenges facing public safety personnel (PSP). Empirical results from longitudinal data collection among PSP remains extremely scant, particularly for institutional correctional workers. We designed the current study to assess the mental health of Correctional Service of Canada (CSC) correctional officer recruits (CORs) across time to help clarify potential challenges to or protective factors for mental health across correctional officer (CO) careers. Methods: The current study uses data from the Canadian Correctional Workers' Wellbeing, Organizations, Roles, and Knowledge (CCWORK) study. The study uses a longitudinal design with self-report surveys administered online prior to CORs beginning the CSC Correctional Training Program. Initial baseline survey data were used to assess demographic information and mental health symptoms endorsed at the outset of the training program. Results: = 265; 40% female; age = 32.8, SD = 9.1) began training between August 2018 and July 2021. Participants were less likely to screen positive for one or more current mental health disorders (i.e., 4.9%) than previously published rates for serving correctional officers (i.e., 54.6%), including reporting lower rates of posttraumatic stress disorder (i.e., 2.4 vs. 29.1%) and major depressive disorder (i.e., 1.9 vs. 31.1%). Conclusion/Impact: Prevalence of positive screens for current mental health disorders in CORs appears lower than for the general population, and significantly lower than for serving correctional officers. The current results suggest an important causal relationship may exist between correctional work and detrimental mental health outcomes. Maintaining the mental health of correctional officers may require institutionally-supported proactive and responsive multimodal activities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".