Cultural Consultation: A Model of Mental Health Service for Multicultural Societies
Bibliographic record
Abstract
OBJECTIVES: This paper reports results from the evaluation of a cultural consultation service (CCS) for mental health practitioners and primary care clinicians. The service was designed to improve the delivery of mental health services in mainstream settings for a culturally diverse urban population including immigrants, refugees, and ethnocultural minority groups. Cultural consultations were based on an expanded version of the DSM-IV cultural formulation and made use of cultural consultants and culture brokers. METHODS: We documented the service development process through participant observation. We systematically evaluated the first 100 cases referred to the service to establish the reasons for consultation, the types of cultural formulations and recommendations, and the consultation outcome in terms of the referring clinician's satisfaction and recommendation concordance. RESULTS: Cases seen by the CCS clearly demonstrated the impact of cultural misunderstandings: incomplete assessments, incorrect diagnoses, inadequate or inappropriate treatment, and failed treatment alliances. Clinicians referring patients to the service reported high rates of satisfaction with the consultations, but many indicated a need for long-term follow-up. CONCLUSION: The cultural consultation model effectively supplements existing services to improve diagnostic assessment and treatment for a culturally diverse urban population. Clinicians need training in working with interpreters and culture brokers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".