Implementing antibiotic stewardship in high-prescribing English general practices: a mixed-methods study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Trials have identified antimicrobial stewardship (AMS) strategies that effectively reduce antibiotic use in primary care. However, many are not commonly used in England. The authors co-developed an implementation intervention to improve use of three AMS strategies: enhanced communication strategies, delayed prescriptions, and point-of-care C-reactive protein tests (POC-CRPTs). AIM: To investigate the use of the intervention in high-prescribing practices and its effect on antibiotic prescribing. DESIGN AND SETTING: Nine high-prescribing practices had access to the intervention for 12 months from November 2019. This was primarily delivered remotely via a website with practices required to identify an 'antibiotic champion'. METHOD: Routinely collected prescribing data were compared between the intervention and the control practices. Intervention use was assessed through monitoring. Surveys and interviews were conducted with professionals to capture experiences of using the intervention. RESULTS: There was no evidence that the intervention affected prescribing. Engagement with intervention materials differed substantially between practices and depended on individual champions' preconceptions of strategies and the opportunity to conduct implementation tasks. Champions in five practices initiated changes to encourage use of at least one AMS strategy, mostly POC-CRPTs; one practice chose all three. POC-CRPTs was used more when allocated to one person. CONCLUSION: Clinicians need detailed information on exactly how to adopt AMS strategies. Remote, one-sided provision of AMS strategies is unlikely to change prescribing; initial clinician engagement and understanding needs to be monitored to avoid misunderstanding and suboptimal use.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it