Using the Cultural Formulation to Resolve Uncertainty in Diagnoses of Psychosis Among Ethnoculturally Diverse Patients
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to assess the impact of systematic use of the DSM-IV-TR cultural formulation on diagnoses of psychotic disorders among patients of ethnic minority and immigrant backgrounds referred to a cultural consultation service (CCS) in Canada. METHODS: The study entailed a review of medical records and case conference transcripts of 323 patients seen in a ten-year period at the CCS to determine factors associated with change in the diagnosis of psychotic disorders by the CCS. Logistic regression analysis was used to identify variables associated with changes in diagnosis. RESULTS: A total of 34 (49%) of the 70 cases with an intake (referral) diagnosis of a psychotic disorder were rediagnosed as nonpsychotic disorders, whereas only 12 (5%) of the 253 cases with an intake diagnosis of a nonpsychotic disorder were rediagnosed as a psychotic disorder (p<.001). Major depression, posttraumatic stress disorder (PTSD), adjustment disorder, and bipolar affective disorder were the common disorders diagnosed with use of the cultural formulation. Rediagnosis of a psychotic disorder as a nonpsychotic disorder was significantly associated with being a recent arrival in Canada (odds ratio [OR]=6.05, 95% confidence interval [CI]=1.56-23.46, p=.009), being nonblack (OR=3.72, CI=1.03-13.41, p=.045), and being referred to the CCS by nonmedical routes (such as social work or occupational therapy) (OR=3.23, CI=1.03-10.13, p=.044). CONCLUSIONS: Misdiagnosis of psychotic disorders occurred with patients of all ethnocultural backgrounds. PTSD and adjustment disorder were misidentified as psychosis among immigrants and refugees from South Asia. Studies are needed that compare clinical outcomes of use of cultural consultation with outcomes from use of other cultural competence models.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".