Education for corrections officers to better meet the mental health needs of inmates
Bibliographic record
Abstract
Aims In Canada, there has been an increase in the rate of incarceration of individuals with mental health diagnoses. Overrepresentation of individuals with psychiatric diagnoses in correctional settings is well-established. Front-line officers play a central role in dealing with mental health struggles of inmates. Nonetheless, the training that officers receive is often considered inadequate. To address this gap, the goal of this study was to design, implement, and evaluate a mental health training for correctional officers at the Toronto South Detention Centre (TSDC) and Vanier Centre for Women (VCW) in Ontario, Canada. Method A needs assessment was undertaken among officers at the TSDC. In response to needs identified, a one-day course was delivered to officers (n = 57) at the TSDC and VCW (n = 41). The curriculum included mental health awareness; assessment of risk; communicating with inmates in distress; and self-care. Live simulations provided the opportunity for participants to identify signs of mental illness, assess risk, and respond strategically to de-escalate situations. Participants’ knowledge and confidence in their ability to identify and assist individuals with these problems was established using pre and post measures. Participant satisfaction was also measured via a survey. A three-month follow-up administration was used to determine maintenance of gains. Focus groups at nine months were conducted to understand participants’ needs, learning, and impact of training. Result The results were promising, with 92% and 88% of participants at TSDC and Vanier Centre for Women respectively expressing satisfaction and 62% and 68% at TSDC and Vanier Centre for Women respectively stating they intended to change practices. Analyses of change in knowledge and confidence scores pre to post-training showed statistically significant improvement in all areas measured. Three-month follow-up at TSDC showed 75% of respondents have applied what they learned from the training to a “moderate or great extent”. Focus group themes showed improved attitudes and ability to identify behaviours related to inmate mental health struggles and interest in further training to support officers’ mental health. Conclusion This study shows that training informed by officer learning needs can help them better meet the mental health needs of inmates. Training can improve attitudes toward inmates presenting with mental health issues. Training that is interactive and provides skills practice can have sustained impact on practice. Further training should integrate self-care to support officers' mental health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".