Regional and national antimicrobial stewardship activities: a survey from the Joint Programming Initiative on Antimicrobial Resistance—Primary Care Antibiotic Audit and Feedback Network (JPIAMR-PAAN)
Bibliographic record
Abstract
Background: Antibiotic overuse and misuse in primary care are common, highlighting the importance of antimicrobial stewardship (AMS) efforts in this setting. Audit and feedback (A&F) interventions can improve professional practice and performance in some settings. Objectives and methods: To leverage the expertise from international members of the Joint Programming Initiative on Antimicrobial Resistance - Primary care Antibiotic Audit and feedback Network (JPIAMR-PAAN). Network members all have experience of designing and delivering A&F interventions to reduce inappropriate antibiotic prescribing in primary care settings. We aim to introduce the network and explore ongoing A&F activities in member regions. An online survey was administered to all network members to collect regional information. Results: Fifteen respondents from 11 countries provided information on A&F activities in their country, and national/regional antibiotic stewardship programmes or policies. Most countries use electronic medical records as the primary data source, antibiotic appropriateness as the main outcome of feedback, and target GPs as the prescribers of interest. Funding sources varied across countries, which could influence the frequency and quality of A&F interventions. Nine out of 11 countries reported having a national antibiotic stewardship programme or policy, which aim to provide systematic support to ongoing AMS efforts and aid sustainability. Conclusions: The survey identified gaps and opportunities for AMS efforts that include A&F across member countries in Europe, Canada and Australia. JPIAMR-PAAN will continue to leverage its members to produce best practice resources and toolkits for antibiotic A&F interventions in primary care settings and identify research priorities.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".