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Record W2277287535 · doi:10.7870/cjcmh-2010-0005

Synthesizing Culture and Power in Community Mental Health: An Emerging Framework

2010· article· en· W2277287535 on OpenAlexaffvenue
Rich Janzen, Joanna Ochocka, Nora Jacobson, Sarah Maiter, Laura Simich, Anne Westhues, Augie Fleras

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2010
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsUniversity of WaterlooWilfrid Laurier UniversityCentre for Addiction and Mental HealthYork UniversityUniversity of TorontoCentre for Community Based Research
Fundersnot available
KeywordsMental healthGeneral partnershipParticipatory action researchAllianceCommunity-based participatory researchCitizen journalismPublic relationsDiversity (politics)Culturally appropriatePower (physics)SociologyPsychologyMedicinePolitical sciencePsychotherapistGerontology

Abstract

fetched live from OpenAlex

Mental health services in western English-speaking countries are struggling to respond to growing cultural and racial diversity. The overall purpose of the Community University Research Alliance (CURA) study was to explore, develop, pilot, and evaluate how best to provide community-based mental health supports that are effective for people from culturally diverse backgrounds. Using a participatory action research approach within a multimethod design, the study partnership has developed an emerging framework that synthesizes the ideals of previous culture-oriented and power-oriented models. The emerging framework has 3 main components: values that guide concrete actions that in turn produce desired outcomes. Central to the emerging framework is the need for reciprocal collaboration between the mental health system and cultural-linguistic 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.

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)0.052
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.052
Threshold uncertainty score0.275

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.031
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0110.007
Science and technology studies0.0160.115
Scholarly communication0.0270.029
Open science0.0060.022
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0020.000

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 head0.131
GPT teacher head0.510
Teacher spread0.379 · 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 designTheoretical or conceptual
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

Citations5
Published2010
Admission routes2
Has abstractyes

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