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Record W2072433762 · doi:10.1177/1363461505055628

Overview of Culturally-Based Mental Health Care in Vancouver

2005· article· en· W2072433762 on OpenAlexaffabout
Soma Ganesan, Teresa Janzé

Bibliographic record

VenueTranscultural Psychiatry · 2005
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsVancouver General HospitalUniversity of British Columbia
Fundersnot available
KeywordsMental healthHealth careMulticulturalismPopulationNursingMedicineSociologyEconomic growthEnvironmental healthPsychiatry

Abstract

fetched live from OpenAlex

This article is a description of how cross-cultural services in mental health have evolved in Vancouver. With 49% of Vancouver's total population described as a 'visible minority' by Statistics Canada, it has been essential for the city, in its efforts to provide health care that is accessible, available and acceptable to all, to develop health care that acknowledges racial and cultural diversities. Vancouver's Cross Cultural Mental Health Services had their beginnings over 25 years ago. The services encompass both formal and informal sectors of the healthcare system, are provided at primary, secondary and tertiary levels of healthcare delivery and are available through hospital- and community-based services. With recent regionalization of British Columbia's health services, the cross-cultural mental health service has experienced increased coordination under the administration of the Vancouver Coastal Health Authority (one of six British Columbia health regions). The initial elements of a cross-cultural mental health service consisted of the Vancouver Association for the Survivors of Torture, the Cross-Cultural Clinic at Vancouver General Hospital, and the Multicultural Liaison Workers Program of the Vancouver Community Mental Health Service. Collaboration and partnerships between the formal and informal sectors support each other, bridge gaps in services and provide a milieu for growth and development.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.023
GPT teacher head0.350
Teacher spread0.327 · 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
Published2005
Admission routes2
Has abstractyes

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