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Record W3139178268 · doi:10.1108/pijpsm-10-2016-0153

Variations in Mental Health Act calls to police: an analysis of hourly and intra-week patterns

2018· article· en· W3139178268 on OpenAlexaff
Adam D. Vaughan, Kathryn Wuschke, Ashley N. Hewitt, Tarah Hodgkinson, Martin A. Andresen, Patricia L. Brantingham, Simon N. Verdun‐Jones

Bibliographic record

VenuePolicing An International Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMental healthContext (archaeology)OriginalityPsychologyService (business)Mental health serviceNames of the days of the weekCriminologyApplied psychologySocial psychologyPsychiatryGeographyBusinessMarketing

Abstract

fetched live from OpenAlex

Purpose Investigating the day of week and hour of day temporal patterns of crime typically show that (late) nights and weekends are the prime time for criminal activity. Though instructive, mental-health-related calls for service are a significant component of police service to the community that have not been a part of this research. The purpose of this paper is to analyze calls for police service that relate to mental health, using intimate partner/domestic related calls for police service for context. Design/methodology/approach Approximately 20,000 mental health related and 20,000 intimate partner/domestic related calls for police service are analyzed. Intra-week and intra-day temporal patterns are analyzed using circular statistics. Findings Mental-health-related calls for police service have a distinct temporal pattern for both days of the week and hours of the day. Specifically, these calls for police service peak during the middle of the week and in the mid-afternoon. Originality/value This is the first analysis regarding the temporal patterns of police calls for service for mental health-related calls. The results have implications for police resourcing and scheduling, especially in the context of special teams for addressing mental health-related calls for police service.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

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

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 head0.056
GPT teacher head0.454
Teacher spread0.398 · 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 teacher head, 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

Citations7
Published2018
Admission routes1
Has abstractyes

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