Adaptation and implementation of the Cultural Formulation Interview and training module for pediatric neuropsychology providers
Bibliographic record
Abstract
Conceptual frameworks for cultural competence are essential in psychological care. The assessment of implementation efforts in pediatric neuropsychological contexts is particularly lacking. This study investigated an adapted training program for the Cultural Formulation Interview (CFI), including impact on provider cultural competence and perspectives on adaptation and implementation. Neuropsychological care providers (N = 16) participated in an adapted, group-based CFI training. The training included review of an online module, supplemented with reflective discussion. Participants completed pre- and post-training measures of perceived cultural competence, as well as a post-training survey assessing acceptability and utility of the CFI and training, facilitators and barriers to implementation, and potential adaptations to the CFI. The CFI and adapted training were rated highly on acceptability and utility. Self-reported confidence and competence in delivering culturally responsive care increased from pre- to post-training (z = −3.4; p = .0007). Key barriers and facilitators to implementation included resource needs, continuing education, and systemic change. Participants identified a theoretical, abbreviated 5-item CFI including alternative wording to fit their clinical needs. Overall, the adapted CFI training enhanced perceived cultural competence among pediatric neuropsychological providers, with strong acceptability and relevance to clinical practice. Recommendations will guide future training and systemic implementation.
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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 | 0.034 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.002 |
| 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".