MétaCan
Menu
Back to cohort
Record W4308917341 · doi:10.1111/ruso.12471

Police Encounters for Behavioral Health‐Related Reasons in Rural and Remote Communities: A Canadian Study<sup>☆</sup>

2022· article· en· W4308917341 on OpenAlex
Jean‐Denis David

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueRural Sociology · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsMcGill University
Fundersnot available
KeywordsMental healthContext (archaeology)Suicide preventionPopulationRural areaPsychologySocial isolationOccupational safety and healthPoison controlHuman factors and ergonomicsInjury preventionRural healthGeographyEnvironmental healthMedicinePsychiatry

Abstract

fetched live from OpenAlex

Abstract Evidence suggests police officers are increasingly called upon to respond to incidents related to mental health issues, emotional problems, and substance abuse. Many have raised concerns regarding their involvement in such incidents. Yet, little is known about these encounters in rural and remote communities despite evidence suggesting that the context of non‐urban areas should matter. Accordingly, this article proposes to examine variations in self‐reported encounters with the police for behavioral health‐related reasons across urban, rural, and remote communities. Using data from the 2014 General Social Survey, a representative sample of the Canadian population, we assess these self‐reported encounters from two different angles: encounters for one's own behavioral health crisis and encounters for a family member's behavioral health needs. While findings on the former are inconclusive, those examining police contacts for a family member suggest that living in rural or remote communities is significantly associated with a greater probability of experiencing such situations relative to living in urban areas. Furthermore, this probability increases with the relative geographical isolation of communities. These results are discussed in light of the rising concerns regarding our reliance on the police for such incidents and the need to account for the situation of rural and remote communities.

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 score0.169
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
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 head0.054
GPT teacher head0.417
Teacher spread0.364 · 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