How to Make Feedback More Effective? Qualitative Findings from Pilot Testing of an Audit and Feedback Report for Endoscopists
Bibliographic record
Abstract
Background. Audit and feedback (A/F) reports are one of the few knowledge translation activities that can effect change in physician behavior. In this study, we pilot-tested an endoscopist A/F report to elicit opinions about the proposed report’s usability, acceptability and usefulness, and implications for knowledge translation.Methods. Semi-structured qualitative interviews were conducted with eleven endoscopists in Ontario, Canada. We tested an A/F report template comprising 9 validated, accepted colonoscopy quality indicators populated with simulated data. Interview transcripts were coded using techniques such as constant comparison and themes were identified inductively over several team meetings.Results. Four interrelated themes were identified: (1) overall perceptions of the A/F report; (2) accountability and consequences for poor performance; (3) motivation to change/improve skills; and (4) training for performance enhancement and available resources. The A/F report was well received; however, participants cited some possible threats to the report’s effectiveness including the perceived threat of loss of privileges or licensing and the potential for the data to be dismissed.Conclusions. Participants agreed that A/F has the potential to improve colonoscopy performance. However, in order to be effective in changing physician behavior, A/F must be thoughtfully implemented with attention to the potential concerns of its recipients.
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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 | 0.105 | 0.261 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".