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Record W7006035995

The Spatial Concentration, Stability, and Specialization of Mental Health Calls for Service: Evidence in Support of Proactive, Place-Based Interventions

2022· article· en· W7006035995 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2022
Typearticle
Languageen
FieldMedicine
TopicBiological and pharmacological studies of plants
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionIntervention (counseling)De factoSoftware deploymentCriminalizationMental healthService (business)Limiting
DOInot available

Abstract

fetched live from OpenAlex

For many decades the police have been the de facto responders to persons with perceived mental illness (PwPMI). However, having the police in this role has come with negative repercussions for PwPMI, such as disproportionately experiencing criminalization and use of force. In recognizing these issues, the police—and more recently, the community—have developed responses that either seek to improve interactions between the police and PwPMI or remove the police from this role altogether. However, in either case, these efforts are reactive in nature, responding to crises that arguably could have been prevented had a timelier intervention taken place. Further, evidence on certain police responses to PwPMI, such as Crisis Intervention Teams (CIT) and co-response teams, suggests that they endure deployment-related challenges, thus limiting their reach to PwPMI.\nDrawing from the Criminology of Place and existing place-based policing strategies, the present dissertation argues that efforts focused on responding to PwPMI should instead be proactively deployed, targeting areas where interactions between police and PwPMI concentrate spatially. Doing so would not only result in efficient deployment of scarce resources but would permit police- and community-based efforts to have a greater reach to PwPMI and thus prevent future interactions with police. To-date, however, there have been few empirical and theoretical investigations into the spatial patterns of PwPMI calls for service that could inform such proactive, place-based efforts. Specifically, we do not currently understand: (1) the degree to which PwPMI calls for service concentrate within certain geographical contexts (such as a small city); (2) whether the degree of PwPMI call concentration and the location of these calls remain stable over time; and (3) what theoretical frameworks explain why PwPMI calls for service occur where they do. Drawing on seven years (2014-2020) of calls for service data from the Barrie Police Service and data from the 2016 Canadian Census, the present dissertation employs various methods of spatial analysis to fills these specific knowledge gaps.\nAlthough the theoretical investigation confirmed the findings of previous work that found no association between social disorganization theory and the spatial patterns of PwPMI calls for service, the present dissertation revealed: (1) PwPMI calls for service are highly concentrated within the context of a small city, even more so than what has previously been uncovered in larger jurisdictions; (2) the degree of PwPMI call concentration is stable over time, falling within a narrow proportional bandwidth of spatial units; and (3) PwPMI calls for service, and their concentrations, occur in the same places over time—even during the COVID-19 pandemic—and are thus spatially stable. As such, though more scholarship is needed on theories that might help explain why PwPMI calls occur where they do, the findings of the present dissertation strongly support the proactive, place-based deployment of resources to PwPMI.

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)0.011
metaresearch head score (Gemma)0.085
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 score0.024
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.085
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.006
Scholarly communication0.0040.004
Open science0.0050.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.283
GPT teacher head0.404
Teacher spread0.120 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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