Interdisciplinary case discussions as a training modality to teach cultural formulation in child mental health
Bibliographic record
Abstract
The DSM-5 Cultural Formulation Interview (CFI) may become an important tool to help operationalize culture in the clinical realm. However, challenges exist in teaching its use to avoid the risk of stereotyping and oversimplification, which could result in misunderstanding and stigma. The aim of this article is to document whether the CFI can be taught using regular Interdisciplinary Case Discussion Seminars (ICDSs), proposed as continuing education in child mental health and as part of clinical rotations for new trainees. During a two-year evaluative research project, ICDSs were held monthly in three different primary care settings servicing recent immigrants in Montreal, Canada. ICDSs were recorded and analyzed to examine their effect on the cultural formulation process and focus groups were conducted to explore the subjective experience of the participant trainees and professionals. Results suggest that ICDSs are a helpful way to teach the use of the CFI. The group discussions helped participants to better capture the complexity of the cultural and social experience of the child and family by moving away from simple identity assignations, supporting an inquiry into structural dimensions, and considering stigma and inequality in their formulation. The multiple levels of diversity (individual, disciplinary, and interinstitutional) represented in the discussion groups helped clinicians to understand the cultural formulation as situated in a specific relational context and a particular moment and, in so doing, helped trainees to avoid cultural formulations that essentialize culture.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".