How do physicians behave when they participate in audit and feedback activities in a group with their peers?
Bibliographic record
Abstract
BACKGROUND: Audit and feedback interventions may be strengthened using social interaction. With this in mind, the Calgary office of the Alberta Physician Learning Program developed a process for audit and group feedback for physician groups. As a part of a larger project to develop a practical approach to the design and implementation of audit and group feedback projects, we explored patterns of physician behavior during facilitated audit and group feedback sessions. METHODS: Six audit and group feedback sessions were recorded, transcribed, and analyzed thematically to derive a conceptual model of physicians' behaviors during audit and group feedback sessions. RESULTS: A predictable cycle of behaviors emerged from audit and group feedback sessions. This cycle would repeat with discussion of each new data element: reacting to the data, questioning and understanding the data, justifying and contextualizing, sharing and reflecting on the data and relevant guidelines, and planning for change. "Change cues" that emerged within groups reliably pivoted the discussion towards action planning. CONCLUSIONS: In audit and group feedback sessions, physicians display a predictable series of behaviors as they move towards commitment to change. Establishing the meaning and credibility of the data is a necessary precursor to reflection. Group reflection leads to "change cues" triggered by group members, which stimulate action planning.
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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 | 0.011 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".