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Record W4312936630 · doi:10.7870/cjcmh-2022-016

Evaluating the Organizational Cultural Competence of a Clinical Psychology Training Clinic: Findings and Implications for Training Sites

2022· article· en· W4312936630 on OpenAlexaffvenue
Marta Y. Young, K. Jacky Chan, Marie-Pier Vandette, Rebecca Cherner, Taylor Hatchard, Dhrasti Shah, Cary S. Kogan

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCultural competenceCompetence (human resources)PsychologyMental healthCultural sensitivityMedical educationApplied psychologyNursingSocial psychologyMedicinePedagogyPsychotherapist

Abstract

fetched live from OpenAlex

Canadians from diverse ethnocultural backgrounds have specific mental health needs. Cultural competence in mental health care enhances client satisfaction and improves outcomes for individuals from minority groups. Few studies examine cultural competence at the organizational level. This program evaluation examined organizational cultural competence of a university-based psychology clinic. Surveys were completed by internal stakeholders and clients. Results suggest a high level of commitment to cultural competence among internal stakeholders. Clients reportedly felt welcomed and that their cultural needs were met. Challenges related to cultural competency training, educational opportunities, policies, and procedures will be discussed. Recommendations have implications for psychology clinics.

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)0.028
metaresearch head score (Gemma)0.059
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score0.643

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.059
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0050.002
Scholarly communication0.0040.002
Open science0.0020.004
Research integrity0.0010.002
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 head0.595
GPT teacher head0.592
Teacher spread0.003 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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