Process evaluation of two large randomized controlled trials to understand factors influencing family physicians’ use of antibiotic audit and feedback reports
Bibliographic record
Abstract
BACKGROUND: Unnecessary antibiotic prescriptions in primary care are common and contribute to antimicrobial resistance in the population. Audit and feedback (A&F) on antibiotic prescribing to primary care can improve the appropriateness of antibiotic prescribing, but the optimal approach is uncertain. We performed two pragmatic randomized controlled trials of different approaches to audit and feedback. The trial results showed that A&F was associated with significantly reducing antibiotic prescribing. Still, the effect size was small, and the modifications to the A&F interventions tested in the trials were not associated with any change. Herein, we report a theory-informed qualitative process evaluation to explore potential mechanisms underlying the observed effects. METHODS: Ontario family physicians in the intervention arms of both trials who were sent A&F letters were invited for one-on-one interviews. Purposive sampling was used to seek variation across interested participants in personal and practice characteristics. Qualitative analysis utilized inductive and deductive techniques informed by the Clinical Performance Feedback Intervention Theory. RESULTS: Modifications to the intervention design tested in the trial did not alter prescribing patterns beyond the changes made in response to the A&F overall for various reasons. Change in antibiotic prescribing in response to A&F depended on whether it led to the formation of specific intentions and whether those intentions translated to particular behaviours. Those without intentions to change tended to feel that their unique clinical context was not represented in the A&F. Those with intentions but without specific actions taken tended to express a lack of self-efficacy for avoiding a prescription in contexts with time constraints and/or without an ongoing patient relationship. Many participants noted that compared to overall prescribing, A&F on antibiotic prescription duration was perceived as new information and easily actionable. CONCLUSION: Our findings indicate that contextual factors, including the types of patients and the setting where they are seen, affect how clinicians react to audit and feedback. These results suggest a need to test tailored feedback reports that reflect the context of how, where, and why physicians prescribe antibiotics so that they might be perceived as more personal and more actionable. TRIAL REGISTRATION: Clinical Trial registration IDs: NCT04594200, NCT05044052.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".