Training on the Cultural Formulation Interview and self-assessed cultural competence in practitioners working in student counseling services
Bibliographic record
Abstract
The use of the Cultural Formulation Interview (CFI), a semi-structured 16-item cultural assessment developed for the DSM-5, has not yet been evaluated in the context of post-secondary education campuses (Cégeps and universities). The aim of this study was to assess whether a 2-h training session on the CFI improved cultural competence in clinicians working in student counseling centers throughout the province of Québec as well as to evaluate the clinicians' attitudes toward the adoption of the CFI and their perceptions of the tool's feasibility, acceptability, and clinical utility. Thirty-nine clinicians from seven Cégeps and universities completed the pre-training questionnaires, participated in the 2-h training session, and then completed the post-training questionnaires. Paired-samples t tests were used to compare change in the scores of self-reported cultural competence pre and post training. Clinicians favorably rated the assessment tool, and most clinicians (89%) reported that they planned to incorporate the CFI into their routine clinical practice. There was an increase in multicultural counseling knowledge post training session, as well as a decrease in the multicultural counseling relationship subscale. Potential barriers to the use of CFI were also identified. More research is needed to explore the use of the CFI in different clinical contexts, as well as to evaluate the impact of cultural competence training on clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".