The Cultural Formulation Interview since DSM-5: Prospects for training, research, and clinical practice
Bibliographic record
Abstract
While social science research has demonstrated the importance of culture in shaping psychiatric illness, clinical methods for assessing the cultural dimensions of illness have not been adopted as part of routine care. Reasons for limited integration include the impression that attention to culture requires specialized skills, is only relevant to a subset of patients from unfamiliar backgrounds, and takes too much time to be useful. The DSM-5 Cultural Formulation Interview (CFI), published in 2013, was developed to provide a simplified approach to collecting information needed for cultural assessment. It offers a 16-question interview protocol that has been field tested at sites around the world. However, little is known about how CFI implementation has affected training, health services, and clinical outcomes. This article offers a comprehensive narrative review that synthesizes peer-reviewed, published studies on CFI use. A total of 25 studies were identified, with sample sizes ranging from 1 to 460 participants. In all pilot CFI studies 960 unique subjects were enrolled, and in final CFI studies 739 were enrolled. Studies focused on how the CFI affects clinical practice; explored the CFI through research paradigms in medical communication, implementation science, and family psychiatry; and examined clinician training. In most studies, patients and clinicians reported that using the CFI improved clinical rapport. This evidence base offers an opportunity to consider implications for training, research, and clinical practice and to identify crucial areas for further research.
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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 | 0.054 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".