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Record W2254499662 · doi:10.7870/cjcmh-2010-0025

Community Mental Health Organizations in Ontario: Perceptions of Cultural Responsiveness

2010· article· en· W2254499662 on OpenAlexafffundvenueabout
Anne Westhues, Rich Janzen, Don Roth, Jill G. Grant

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsUniversity of WindsorCanadian Mental Health AssociationCentre for Community Based ResearchCommunity Based Research CentreWilfrid Laurier University
FundersOntario Trillium Foundation
KeywordsMental healthPerceptionPublic relationsWork (physics)PsychologyService (business)Baseline (sea)Mental health serviceCultural issuesCultural diversitySociologyPolitical scienceBusinessMarketingPsychiatryEngineering

Abstract

fetched live from OpenAlex

This paper reports the findings from an online survey that explored the perceptions of 111 leaders within community mental health organizations in Ontario about how responsive they are to the service needs of people from diverse cultural-linguistic groups. The findings show that more than half of respondents said they engaged in 20 of the 27 practices that promote cultural responsiveness. Comparisons of community organizations with different service philosophies found only one difference in terms of engaging in culturally responsive practices. Specifically, the difference was in whether or not staff received ongoing training in how to work with people from the cultural-linguistic groups the organization serves. These data provide a baseline against which progress can be measured toward greater cultural responsiveness in community mental health organizations in Ontario.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.004
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.055
GPT teacher head0.377
Teacher spread0.322 · 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 designQualitative
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

Citations4
Published2010
Admission routes4
Has abstractyes

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