Culture and Psychiatric Evaluation: Operationalizing Cultural Formulation for<i>DSM-5</i>
Bibliographic record
Abstract
The Outline for Cultural Formulation (OCF) introduced with DSM-IV provided a framework for clinicians to organize cultural information relevant to diagnostic assessment and treatment planning. However, use of the OCF has been inconsistent, raising questions about the need for guidance on implementation, training, and application in diverse settings. To address this need, DSM-5 introduced a cultural formulation interview (CFI) that operationalizes the process of data collection for the OCF. The CFI includes patient and informant versions and 12 supplementary modules addressing specific domains of the OCF. This article summarizes the literature reviews and analyses of experience with the OCF conducted by the DSM-5 Cross-Cultural Issues Subgroup (DCCIS) that informed the development of the CFI. We review the history and contents of the DSM-IV OCF, its use in training programs, and previous attempts to render it operational through questionnaires, protocols, and semi-structured interview formats. Results of research based on the OCF are discussed. For each domain of the OCF, we summarize findings from the DCCIS that led to content revision and operationalization in the CFI. The conclusion discusses training and implementation issues essential to service delivery.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".