Behavioral Nudges to Improve Audit and Feedback Report Opening Among Antibiotic Prescribers: A Randomized Controlled Trial
Bibliographic record
Abstract
Abstract Background Peer comparison audit and feedback has demonstrated effectiveness in improving antibiotic prescribing practices, but only a minority of prescribers view their reports. We rigorously tested 3 behavioral nudging techniques delivered by email to improve report opening. Methods We conducted a pragmatic randomized controlled trial among Ontario long-term care prescribers enrolled in an ongoing peer comparison audit and feedback program which includes data on their antibiotic prescribing patterns. Physicians were randomized to 1 of 8 possible sequences of intervention/control allocation to 3 different behavioral email nudges: a social peer comparison nudge (January 2020), a maintenance of professional certification incentive nudge (October 2020), and a prior participation nudge (January 2021). The primary outcome was feedback report opening; the primary analysis pooled the effects of all 3 nudging interventions. Results The trial included 421 physicians caring for >28 000 residents at 450 facilities. In the pooled analysis, physicians opened only 29.6% of intervention and 23.9% of control reports (odds ratio [OR], 1.51 [95% confidence interval {CI}, 1.10–2.07], P = .011); this difference remained significant after accounting for physician characteristics and clustering (adjusted OR [aOR], 1.74 [95% CI, 1.24–2.45], P = .0014). Of individual nudging techniques, the prior participation nudge was associated with a significant increase in report opening (OR, 1.62 [95% CI, 1.06–2.47], P = .026; aOR, 2.16 [95% CI, 1.33–3.50], P = .0018). In the pooled analysis, nudges were also associated with accessing more report pages (aOR, 1.28 [95% CI, 1.14–1.43], P < .001). Conclusions Enhanced nudging strategies modestly improved report opening, but more work is needed to optimize physician engagement with audit and feedback. Clinical Trials Registration NCT04187742.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".