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Record W3196079313 · doi:10.1139/facets-2021-0055

Caught in the currents: evaluating the evidence for common downstream police response interventions in calls involving persons with mental illness

2021· article· en· W3196079313 on OpenAlexaffvenueabout
Laura Huey, Judith P. Andersen, Craig Bennell, Mary Ann Campbell, Jacek Koziarski, Adam D. Vaughan

Bibliographic record

VenueFACETS · 2021
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of New BrunswickCarleton UniversityUniversity of TorontoWestern University
Fundersnot available
KeywordsLonelinessMental illnessMental healthPsychological interventionFeelingPsychiatryPsychologyPandemicCriminologyMedicineCoronavirus disease 2019 (COVID-19)Social psychologyDisease

Abstract

fetched live from OpenAlex

The origins of this report, and of the Mental Health and Policing Working Group, can be traced to the unique situation Canadians have faced as a result of the COVID-19 pandemic. The unique circumstances of this global outbreak, which have for many Canadians resulted in serious illness and death, intensified economic uncertainties, altered family and lifestyle dynamics, and generated or exacerbated feelings of loneliness and social dislocation, rightly led the Royal Society of Canada’s COVID-19 Taskforce to consider the strains and other negative impacts on individual, group, and community mental health. With the central role that police too often play in the lives of individuals in mental and (or) emotional crisis, we were tasked with exploring what can be reasonably said about the state of our current knowledge of police responses to persons with mental illness.

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)0.104
metaresearch head score (Gemma)0.414
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: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.550

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1040.414
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0040.004
Science and technology studies0.0040.008
Scholarly communication0.0060.009
Open science0.0050.007
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0110.001

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 head0.227
GPT teacher head0.479
Teacher spread0.252 · 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

Citations17
Published2021
Admission routes3
Has abstractyes

Explore more

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