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Record W2071316618 · doi:10.7870/cjcmh-2010-0035

Psychiatric Crisis Services in Three Communities

2010· article· en· W2071316618 on OpenAlexaffvenue
Cheryl Forchuk, Elsabeth Jensen, Mary‐Lou Martin, Rick Csiernik, Heather Atyeo

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2010
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsBarrie Urology GroupMcMaster UniversityThe King's UniversitySt. Joseph’s Healthcare HamiltonYork UniversityLawson Health Research Institute
Fundersnot available
KeywordsMental healthMental health serviceService (business)Focus groupSuicide preventionPublic healthMental illnessBusinessPsychologyPublic relationsPoison controlMedicinePsychiatryNursingEnvironmental healthPolitical scienceMarketing

Abstract

fetched live from OpenAlex

This study compared communities with three models of crisis service: (a) police as part of a specialized mental health team, (b) mental health worker as part of a specialized police team, and (c) informal relationship between police and mental health crisis service. Rural and urban areas were examined and compared. Data included focus groups and participant observation. Analysis revealed that while all communities valued their crisis services, all identified limitations in responsiveness, access, and systems-related issues. Quick access to psychiatric beds was important to services. Rural communities had no public transportation, and an important police role was safe transportation. In rural communities, mental health workers were generalists because they had to be able to address situations on their own. In urban areas, transportation was more readily available, and more specialization developed among mental health team members.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.004
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 head0.037
GPT teacher head0.340
Teacher spread0.302 · 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.

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

Citations28
Published2010
Admission routes2
Has abstractyes

Explore more

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