Update on the Cultural Formulation Interview
Bibliographic record
Abstract
. The CFI is an interview protocol designed to be used by clinicians in any setting to gather essential data to produce a cultural formulation. The CFI aims to improve culturally sensitive diagnosis and treatment by focusing clinical attention on the patient's perspective and social context. Preliminary evidence indicates that the CFI can improve clinical communication by enhancing clinician-patient rapport, allowing the clinician to obtain new, cultural data in a relatively short period, eliciting patients' perspectives on what caused their symptoms, and helping patients to become aware of their problems in more insightful ways. With practice, the CFI takes approximately 20 minutes to complete. The CFI has been evaluated internationally in the United States, Canada, Kenya, Peru, the Netherlands, India, and Mexico and generally has been found to be clinically acceptable and useful in these varied settings. Clinicians receiving as little as one hour of training on the CFI improved their ability to work with culturally diverse patients. The CFI may be more difficult to conduct with patients who have severe symptoms, including acute psychosis, suicidal behavior, aggression, and cognitive impairment. The CFI provides a simple way to begin the process of cultural assessment, and its systematic use can foster a reflective stance and promote systemic thinking in routine clinical practice about the patient's life and experience.
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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 | 0.032 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".