A Framework for Developing Mental Health Educational Interventions for Correctional Officers.
Bibliographic record
Abstract
The overrepresentation of persons with mental illness in carceral settings has led to justifiable concerns about their wellbeing and the appropriateness of their care. Correctional officers may be the first point of recognition and management for incarcerated persons experiencing mental illness. Correctional officers thus unknowingly participate in mental health care without a formally recognized mental health care role or knowledge of mental health educational best practice standards. This article reviews the small literature linking specific factors in mental health educational interventions for correctional officers to improvement in knowledge, skills, attitudes, and potentially to incarcerated persons' mental health outcomes. Synthesizing that literature with the authors' experience in creating a mental health educational program for correctional officers at a large provincial detention center in Ontario, Canada, we propose a five-principle framework to guide such programs. We propose such programs be intentionally designed and evaluated with educational and quality improvement best practices in mind, an inclusive attitude toward participants and the intersectional factors in mental health in carceral settings, the use of interactive teaching methods, consideration of instructor relatability, and integration of educational programs into broader philosophical changes in carceral institutions.
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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 | 0.040 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".