Fostering “Reflection-On-Practice” Through a Multisource Feedback and Peer Coaching Pilot Program
Bibliographic record
Abstract
INTRODUCTION: Reflective practice involves thinking about one's practice and often involves using data to effect such reflection. Multisource feedback (MSF) involves evaluation by peers, patients, and coworkers. Coaching has been identified as a key aspect of MSF with peer coaching involving two or more colleagues working together to reflect on current practices and share ideas. We introduced a pilot MSF and peer coaching program with a goal to evaluate its effect on fostering reflective practice. METHODS: Physician participants completed a 360-degree assessment of their practices, followed by peer coaching sessions. Peer coaches were oriented to an evidence-based theory-driven feedback model (R2C2) to support coaching skills development. A mixed-methods evaluation study was undertaken, including pre to post surveys of readiness for self-directed learning, a postevaluation survey of participant satisfaction, and semistructured participant interviews. RESULTS: Thirty four (N = 34) participants completed the 360-degree assessment, and 22 participants took part in two coaching meetings. Respondents reported significant improvement to aspects of their readiness for self-directed learning ( P <.05), including knowing about learning strategies to achieve key learning goals, knowing about resources to support one's own learning, and being able to evaluate one's learning outcomes. Overall, respondents felt empowered to "reflect" on their practices, affirm what they were doing well, and, for some, identify opportunities for further and ongoing professional development. DISCUSSION: MSF and peer coaching emerged as key elements in enabling reflective practice by facilitating reflection on one's practice and conversations with one's peers to affirm strengths and opportunities for strengthening practice through self-directed professional development.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".