Optimizing responsiveness to feedback about antibiotic prescribing in primary care: protocol for two interrelated randomized implementation trials with embedded process evaluations
Bibliographic record
Abstract
BACKGROUND: Audit and feedback (A&F) that shows how health professionals compare to those of their peers, can be an effective intervention to reduce unnecessary antibiotic prescribing among family physicians. However, the most impactful design approach to A&F to achieve this aim is uncertain. We will test three design modifications of antibiotic A&F that could be readily scaled and sustained if shown to be effective: (1) inclusion of case-mix-adjusted peer comparator versus a crude comparator, (2) emphasizing harms, rather than lack of benefits, and (3) providing a viral prescription pad. METHODS: We will conduct two interrelated pragmatic randomized trials in January 2021. One trial will include family physicians in Ontario who have signed up to receive their MyPractice: Primary Care report from Ontario Health ("OH Trial"). These physicians will be cluster-randomized by practice, 1:1 to intervention or control. The intervention group will also receive a Viral Prescription Pad mailed to their office as well as added emphasis in their report on use of the pad. Ontario family physicians who have not signed up to receive their MyPractice: Primary Care report will be included in the other trial administered by Public Health Ontario ("PHO Trial"). These physicians will be allocated 4:1 to intervention or control. The intervention group will be further randomized by two factors: case-mix adjusted versus unadjusted comparator and emphasis or not on harms of antibiotics. Physicians in the intervention arm of this trial will receive one of four versions of a personalized antibiotic A&F letter from PHO. For both trials, the primary outcome is the antibiotic prescribing rate per 1000 patient visits, measured at 6 months post-randomization, the primary analysis will use Poisson regression and we will follow the intention to treat principle. A mixed-methods process evaluation will use surveys and interviews with family physicians to explore potential mechanisms underlying the observed effects, exploring targeted constructs including intention, self-efficacy, outcome expectancies, descriptive norms, and goal prioritization. DISCUSSION: This protocol describes the rationale and methodology of two interrelated pragmatic trials testing variations of theory-informed components of an audit and feedback intervention to determine how to optimize A&F interventions for antibiotic prescribing in primary care. TRIAL REGISTRATION: NCT04594200, NCT05044052. CIHR Grant ID: 398514.
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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 | 0.111 | 0.118 |
| Meta-epidemiology (narrow) | 0.012 | 0.006 |
| Meta-epidemiology (broad) | 0.016 | 0.012 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.097 | 0.020 |
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".