Audit and feedback interventions involving pharmacists to influence prescribing behaviour in general practice: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Pharmacists, as experts in medicines, are increasingly employed in general practices and undertake a range of responsibilities. Audit and feedback (A&F) interventions are effective in achieving behaviour change, including prescribing. The extent of pharmacist involvement in A&F interventions to influence prescribing is unknown. This review aimed to assess the effectiveness of A&F interventions involving pharmacists on prescribing in general practice compared with no A&F/usual care and to describe features of A&F interventions and pharmacist characteristics. METHODS: Electronic databases (MEDLINE, EMBASE, CINAHL, Cochrane Central Register of Controlled Trials, (Social) Science Citation Indexes, ISI Web of Science) were searched (2012, 2019, 2020). Cochrane systematic review methods were applied to trial identification, selection, and risk of bias. Results were summarized descriptively and heterogeneity was assessed. A random-effects meta-analysis was conducted where studies were sufficiently homogenous in design and outcome. RESULTS: Eleven cluster-randomized studies from 9 countries were included. Risk of bias across most domains was low. Interventions focussed on older patients, specific clinical area(s), or specific medications. Meta-analysis of 6 studies showed improved prescribing outcomes (pooled risk ratio: 0.78, 95% confidence interval: 0.64-0.94). Interventions including both verbal and written feedback or computerized decision support for prescribers were more effective. Pharmacists who received study-specific training, provided ongoing support to prescribers or reviewed prescribing for individual patients, contributed to more effective interventions. CONCLUSIONS: A&F interventions involving pharmacists can lead to small improvements in evidence-based prescribing in general practice settings. Future implementation of A&F within general practice should compare different ways of involving pharmacists to determine how to optimize effectiveness.PRISMA-compliant abstract included in Supplementary Material 1.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.030 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".