MP29: Using the Calgary audit and feedback framework to get the most out of physician practice reports
Bibliographic record
Abstract
Innovation Concept: The Calgary Audit and Feedback Framework (CAFF) is an innovative tool developed by the Physician Learning Program (PLP). By addressing four key factors –relationships, question choice, data visualization, and facilitation – CAFF addresses common barriers to physicians receiving their practice data. The goal of this study is to assess whether CAFF-facilitated physician performance improvement (PPI) sessions: 1) improve physicians’ receptiveness to their practice data, and 2) encourage physicians to both identify opportunities for practice change and create action plans. Methods: Peer facilitators were trained to facilitate PPI sessions using the CAFF model. In Calgary, 51/180 emergency physicians have attended at least one of the six PPI sessions. The sessions were evaluated using surveys, commitment to change forms, and the Feedback Orientation Scale (FOS). The FOS is a scale developed to measure a participant's orientation to performance feedback across the four domains of utility, accountability, social awareness, and feedback self-efficacy. Curriculum, Tool, or Material: The PLP has developed and implemented CAFF as a framework to help foster socially constructed learning in audit and group feedback sessions. The CAFF model ensures that the aforementioned four key factors are considered for design and implementation of audit and group feedback. The PLP found that establishing the meaning and credibility of the data is a necessary precursor to reflection and action planning. Conclusion: The FOS was completed for 25/32 physicians. The mean FOS score improved by 0.339 (p < 0.001; z=−3.863). While the mean scores all four domains increased, ‘Feedback Self-Efficacy’ increased the most by .0620 (p < 0.001; z=−3.999). Participants reported that examples of changes made by the peer facilitators were particularly helpful. Evaluations from the sessions suggested physicians overwhelmingly agreed or strongly agreed that the peer comparison was valuable, that the reports helped them reflect on their practice, and that the session helped them identify learning opportunities and strategies to change their practice.
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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 | 0.060 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".