Motivational Interviewing Education in North American Family Medicine Clerkships: A CERA Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Many health conditions are preventable or modifiable through behavioral changes. Motivational interviewing (MI) is an evidence-based communication technique that explores a patient's reasons for behavioral changes. This study assesses the current landscape of MI training in North American Family Medicine (FM) clerkships. METHODS: We analyzed data gathered as part of the 2022 Council of Academic Family Medicine's Educational Research Alliance (CERA) survey of FM clerkship directors (CDs). The survey was distributed via email invitation to 159 US and Canadian FM CDs in June 2022. RESULTS: Of the 94 responses received, 61% indicated that MI training is provided in their FM clerkship. Medical school type, class size, and location were associated with MI training priority, offerings, and duration in the clerkship, respectively. CD experience correlated with MI training duration; student MI skill training level was associated with MI training duration and priority; the rigor of student MI skills evaluation was correlated with MI teaching methods and training duration; self-reported student MI competency was associated with the length of time students spent with FM community preceptors as well as MI training priority and teaching methods; and several items emerged as predictors of student, CD, and FM faculty MI training expansion. CONCLUSIONS: Opportunities exist to enhance the volume, content, and rigor of MI training in North American FM clerkships as well as to improve self-reported student MI competency within those clerkships.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".