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Record W3175594707 · doi:10.1192/bjo.2021.104

Mental health training for correctional officers: a systematic review

2021· review· en· W3175594707 on OpenAlexaff
Shaheen Darani, Sandy Simpson, Robert McMaster, Elena Wolff, Sarah Bonato, Graham Glancy, Jason Quinn

Bibliographic record

VenueBJPsych Open · 2021
Typereview
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of TorontoWestern UniversityCentre for Addiction and Mental Health
Fundersnot available
KeywordsMental healthOfficerMental illnessMedicinePsychologyPsychiatryNursingMedical education

Abstract

fetched live from OpenAlex

Aims Mental illness amongst prison inmates is a prevailing issue across the world, as mental illnesses are overrepresented in correctional facilities when compared to community populations. Despite this, correctional officers receive little to no training on how to respond to inmates with mental illness. Implementing mental health training could improve officer knowledge, skills, and attitudes toward inmates with mental illness. This could lead to improvements in risk management, humane treatment of inmates, and interprofessional collaboration with healthcare providers. There is limited research on the educational value of inmate mental health training programs for correctional officers. As far as we are aware, there have been no prior reviews of this literature. The goal of the present study is to review this literature to explore the nature and effectiveness of correctional officer mental health training programs. Method Medical and criminal justice databases were searched for scientific articles describing correctional officer mental health training programs. All studies that included a measurable outcome on either correctional officer knowledge or inmate mental health were included in a final analysis. The review adhered to PRISMA guidelines for systematic reviews. Result Of 1492 articles identified using search terms, 11 were included in the analysis. 6 articles described mental health education programs, 2 articles described skill-specific programs, and 3 articles described suicide prevention programs. Training programs reviewed content about mental illness, practical skills, and included didactic and experiential teaching modalities. The programs led to improvements in knowledge, skills, and attitudes amongst officers. Prior mental health attitudes, knowledge, and work experience did not correlate with improvements following training. Officers were more receptive to program facilitators with correctional or lived mental health experience. Experiential teaching was preferred to didactic teaching. A decline in training improvements occurred several months after training. Conclusion There is limited but positive literature suggesting that structured training programs, particularly involving persons with lived experience and experiential components are beneficial. The decline in training improvements suggests need for ongoing education and systems change within correctional institutions to ensure sustainability of gains. In terms of limitations of this review, it is possible articles pertaining to correctional officer mental health training were not available on the databases searched or some programs may not be published. Studies were also limited in their outcome measurement, with no consistent tools, and no control groups. This review can guide the development, delivery, and contribute toward best practice guidelines for future inmate mental health training programs and studies.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0090.011
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.307
GPT teacher head0.525
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

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