Snakes and ladders: A qualitative study understanding the active ingredients of social interaction around the use of audit and feedback
Bibliographic record
Abstract
Explore characteristics of the facilitator, group, and interaction that influence whether a group discussion about data leads to the identification of a clearly specified action plan. Peer-facilitated group discussions among primary care physicians were carried out and recorded. A follow-up focus group was conducted with peer facilitators to explore which aspects of the discussion promoted action planning. Qualitative data was analyzed using an inductive-deductive thematic analysis approach using the conceptual model developed by Cooke et al. Group discussions were coded case-specifically and then analyzed to identify which themes influenced action planning as it relates to performance improvement. Physicians were more likely to interact with practice-level data and explore actions for performance improvement when the group facilitator focused the discussion on action planning. Only one of the three sites (Site C) converged on an action plan following the peer-facilitated group discussion. At Site A, physicians shared skepticism of the data, were defensive about performance, and explained performance as a product of factors beyond their control. Site B identified several potential actions but had trouble focusing on a single indicator or deciding between physician- and group-level actions. None of the groups discussed variation in physician-level performance indicators, or how physician actions might contribute to the reported outcomes. Peer facilitators can support data interpretation and practice change; however their success depends on their personal beliefs about the data and their ability to identify and leverage change cues that arise in conversation. Further research is needed to understand how to create a psychologically safe environment that welcomes open discussion of physician variation.
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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 | 0.037 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".