Introduction of a Motivational Interviewing Curriculum for Family Medicine Residents.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The literature has shown that Motivational Interviewing (MI) education can be successfully implemented within the residency education environment; however, little research has described how to implement such a curriculum. We propose that residents' MI skills and confidence will increase following participation in an MI curriculum. METHODS: Thirty family medicine residents were invited to participate in an 8-hour MI curriculum. Residents completed pre- and post-course confidence questionnaires and the Helpful Responses Questionnaire (HRQ). Wilcoxin Rank Test was used to assess rank differences between pre- and post-course self-confidence ratings, and paired t tests were used to assess effect sizes of the curriculum on HRQ responses. Study outcomes included residents' self-perceived confidence in using MI skills and MI skill performance measured by HRQ scores. RESULTS: Residents demonstrated an increase in confidence ratings after the course (n=21, mean before course: 4.19, 2.1 SD; mean after course: 6.71, SD 1.1). Both reviewers found an improvement in HRQ scores after course completion (Reviewer 1: Mean difference=6.05, 95% CI=2.83--8.26, Reviewer 2: Mean difference 4.19, 95% CI=2.19--6.19). CONCLUSIONS: There was a statistically significant increase in residents' self-confidence ratings, as well as an improvement in MI skills post-intervention, as evidenced by a statistically significant improvement in MI-consistent HRQ scores.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".