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Record W2580979502 · doi:10.1111/eip.12422

Examination of cultural competence in service providers in an early intervention programme for psychosis in Montreal, Quebec: Perspectives of service users and treatment providers

2017· article· en· W2580979502 on OpenAlexafffundabout
Shruthi Venkataraman, Gerald Jordan, Megan A. Pope, Srividya N. Iyer

Bibliographic record

VenueEarly Intervention in Psychiatry · 2017
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
FundersCanadian Institutes of Health Research
KeywordsService providerCultural competenceCompetence (human resources)Service delivery frameworkService designService (business)NursingPsychologyMedicineBusinessSocial psychologyMarketingPedagogy

Abstract

fetched live from OpenAlex

AIM: To better understand cultural competence in early intervention for psychosis, we compared service users' and service providers' perceptions of the importance of providers being culturally competent and attentive to aspects of culture. METHODS: At a Canadian early intervention programme, a validated scale was adapted to assess service user (N = 51) and provider (N = 30) perceptions of service providers' cultural competence and the importance accorded thereto. RESULTS: Analyses of variance revealed that the importance of service providers being culturally competent was rated highest by service providers, followed by visible minority service users, followed by white service users. Providers rated themselves as being more interested in knowing about service users' culture than service users perceived them to be. CONCLUSIONS: Service users accorded less import to service providers' cultural competence than providers themselves, owing possibly to varied socialization. A mismatch in users' and providers' views on providers' efforts to know their users' cultures may influence mental healthcare outcomes.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.380
Teacher spread0.337 · 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

Citations4
Published2017
Admission routes3
Has abstractyes

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