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Record W2608036254 · doi:10.1108/jcrpp-01-2017-0005

A focus for mental health training for police

2017· article· en· W2608036254 on OpenAlexaboutno aff
Stuart Thomas, Amy C. Watson

Bibliographic record

VenueJournal of Criminological Research Policy and Practice · 2017
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthPsychologyPsychological interventionTraining (meteorology)Experiential learningMedical educationExperiential knowledgeNursingAndragogyApplied psychologyMedicinePublic relationsPsychiatryAdult educationPedagogyPolitical science

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to propose a focus for mental health training efforts to better equip officers to provide interventions and supports to help facilitate improved outcomes for people experiencing mental health crises. Design/methodology/approach A reflection on key evidence relating to mental health training programmes delivered to police, focussing on Australia, the USA and Canada. Findings While there are a number of similarities in the core content of mental health training programmes offered internationally, the availability and uptake of training across jurisdictions remains piecemeal and idiosyncratic. Police officers report a strong preference for hands-on experiential learning; this has immediate and direct relevance to their operational duties, and is consistent with core principles of andragogy. While all police employees require mental health training, specialised mental health training programmes should clearly be reserved for a select group of officers who volunteer after acquiring sufficient operational experience. Research limitations/implications Priorities should centre on measuring the effectiveness of mental health training packages and discerning the active elements associated with changes in police skills and confidence, as well as identifying elements that support improved outcomes for people who experience mental illness and who have contact with the police. Practical implications Police need to continue to need to seek legitimacy with respect to their guardianship role as mental health interventionists. Training should tap into practice-based wisdom. Training should be practical, applied and reinforced through wider knowledge-based learning and workplace reinforcement. Training is needed for everyone, but specialised training is not for all. Police need to focus on the partnerships and expend time, energy and resources to maintain and grow them. Specialist (and other forms of) training needs to be evaluated so we understand what works? Originality/value There may be opportunities to streamline the delivery of knowledge-based aspects of mental health training and focus much more on experiential learning, both in specialised training courses as well as shorter mental health awareness sessions.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: yes · About a Canadian topic: yes
Qualitativehigh
gptno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: yes
Not applicablehigh
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.033
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.881
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.714
GPT teacher head0.643
Teacher spread0.070 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designQualitative · Not applicable
Domainnot available
GenreEmpirical · Commentary

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

Citations25
Published2017
Admission routes1
Has abstractyes

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