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Record W2020684853 · doi:10.1080/15614261003701665

Canadian police agencies and their interactions with persons with a mental illness: a systems approach

2010· article· en· W2020684853 on OpenAlexaffabout
Dorothy Cotton, Terry Coleman

Bibliographic record

VenuePolice Practice and Research · 2010
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsMental illnessMental healthCriminal justiceService (business)Work (physics)Public relationsSocial workMental health serviceEconomic JusticePsychologyBusinessPolitical scienceCriminologyPsychiatryEngineeringLawMarketing

Abstract

fetched live from OpenAlex

In the 1980s, Canadian police began to experience different demands on their resources with respect to their interactions with persons with a mental illness (PMI) who were in crisis. Deinstitutionalization of PMI and an increasing emphasis on individual rights required a different business model. Change initially was slow but progressive police leaders soon realized that a systems approach was necessary to work cooperatively and collaboratively with other elements of the social service system, such as mental health and the wider criminal justice systems. Subsequently, across Canada various models were designed and programs delivered to better serve those with a mental illness, in particular when they are in crisis; these models now enable police and mental health service providers to better deploy their respective resources.

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)0.007
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.287
Threshold uncertainty score0.827

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.010
Science and technology studies0.0280.024
Scholarly communication0.0220.007
Open science0.0050.009
Research integrity0.0030.004
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 head0.096
GPT teacher head0.466
Teacher spread0.369 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations84
Published2010
Admission routes2
Has abstractyes

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