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Record W2155313770 · doi:10.1177/1363461512439740

Organizational cultural competence consultation to a mental health institution

2012· article· en· W2155313770 on OpenAlexaff
Kenneth Fung, Hung-Tat Lo, Rani Srivastava, Lisa Andermann

Bibliographic record

VenueTranscultural Psychiatry · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCultural competenceCompetence (human resources)Health carePsychologyMental healthCompetence-based managementCultural diversityCore competencyNursingMedicineSociologyStrategic planningPolitical scienceBusinessPedagogySocial psychology

Abstract

fetched live from OpenAlex

Cultural competence is increasingly recognized as an essential component of effective mental health care delivery to address diversity and equity issues. Drawing from the literature and our experience in providing cultural competence consultation and training, the paper will discuss our perspective on the foundational concepts of cultural competence and how it applies to a health care organization, including its programs and services. Based on a recent consultation project, we present a methodology for assessing cultural competence in health care organizations, involving mixed quantitative and qualitative methods. Key findings and recommendations from the resulting cultural competence plan are discussed, including core principles, change strategies, and an Organizational Cultural Competence Framework, which may be applicable to other health care institutions seeking such changes. This framework, consisting of eight domains, can be used for organizational assessment and cultural competence planning, ultimately aiming at enhancing mental health care service to the diverse patients, families, and 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)0.014
metaresearch head score (Gemma)0.033
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 score0.014
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.033
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0140.004
Scholarly communication0.0040.002
Open science0.0020.011
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0140.001

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 head0.036
GPT teacher head0.362
Teacher spread0.326 · 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

Citations87
Published2012
Admission routes1
Has abstractyes

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