The current state of mental health and existing resources for correctional officers in British Columbia
Bibliographic record
Abstract
Purpose To assess the current state of mental health and the resources available for correctional officers working in Provincial correctional facilities in British Columbia. The amount of existing literature focusing on Canadian correctional officers as first responders are minimal, with very few focusing on the officers’ mental health. Methods Surveys were distributed to 1374 unionized employees in Provincial correctional centres in British Columbia. A mixed-methods approach utilized the SF-36v2 quantitative health survey and open-ended interview-style qualitative questions coded using convergent grounded theory. Results Utilizing a convergent approach to data analysis, SF-36v2 data was analyzed using z-score transformations and comparing results to t-scores derived from the 2009 United States general health survey. The average mental health component score was 34.28 [N=196], with 70% [N=196] meeting the criteria for first-stage depression screening. The general population MCS is 50, with 19% meeting depression criteria. Qualitative themes including abandonment, mortality and death were prevalent. Conclusions The correctional officers surveyed scored significantly lower in every test of mental health and wellness than the general population, with men reporting lower mental wellness than women. Officers reported feeling abandoned by the organization and unable to access proper resources. The employer should make significant efforts to improve their employees’ organizational culture and mental health, such as mandatory psychological check-ins and support for officers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".