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Record W2509050873 · doi:10.35502/jcswb.9

A strategic approach to police interactions with people with a mental illness

2016· article· en· W2509050873 on OpenAlex
Terry Coleman, Dorothy Cotton

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueJournal of Community Safety and Well-Being · 2016
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsMental healthMental illnessPublic relationsPsychologyOrder (exchange)CriminologyPolitical scienceBusinessPsychiatryFinance

Abstract

fetched live from OpenAlex

Since the birth of modern policing in the early 1800s, police agencies have interacted with persons with mental health problems (P/MHP) whether in crisis, as victims, or in a support role. Given the nature of policing, this is unlikely to change. What has changed is how police handle these situations. This paper identifies and explains the two phases of the evolution, to date, of police responses and the now necessary third phase. It is time for police agencies to apply a focussed corporate approach to this important social issue and to establish a mental health strategy (third generation) in order to clearly take a strategic approach in concert with relevant community agencies to improve outcomes for P/MHP who come into contact with police personnel. While many standalone programs have been primarily reactive, this paper makes the case that a strategic approach enables the design and implementation of multiple programs congruent with the mental health strategy that are proactive as well as reactive, all with the aim of improving the outcomes for persons with mental illness and mental health problems.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.087
GPT teacher head0.370
Teacher spread0.283 · 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