Interprofessional Communities of Practice in Continuing Medical Education for Promoting and Sustaining Practice Change: A Prospective Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Standard knowledge delivery formats for CME may have limited impact on long-term practice change. A community of practice (CoP) is one tool that may enhance competencies and support practice change. This study explores the utility of an interprofessional CoP as an adjunct to a CME program in tobacco addiction treatment (Training Enhancement in Applied Counselling and Health [TEACH] Project) to promote and sustain practice change. METHODS: A prospective cohort design was utilized to examine the long-term impact of the TEACH CoP on practice change. An online survey was administered to TEACH-trained practitioners to assess perceived feasibility, importance, and confidence related to course competencies, involvement in TEACH CoP activities, engagement in knowledge transfer (KT), and implementation of new programming. Chi-square tests were used to detect differences in KT and program development associated with CoP participation. Course competency scores from immediate postcourse surveys and long-term follow-up surveys were compared. RESULTS: No significant differences in participant characteristics were found between those who did (n = 300) and did not (n = 122) participate in the TEACH CoP. Mean self-perceived competency scores were greater immediately after course than at long-term follow-up; however, self-ratings of competency in pharmacological interventions and motivational interviewing were higher at follow-up. TEACH CoP participation was associated with significantly greater engagement in KT and implementation of new programming after training. DISCUSSION: The findings from this evaluation suggest the value of interprofessional CoPs offered posttraining as a mechanism to enhance practice. CME providers should consider offering CoPs as a component of training programs to promote and sustain practice change.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".