The feasibility and generalizability of assessing the appropriateness of antimicrobial prescribing in hospitals: a review of the Australian National Antimicrobial Prescribing Survey
Bibliographic record
Abstract
The National Antimicrobial Prescribing Survey (NAPS) is a web-based qualitative auditing platform that provides a standardized and validated tool to assist hospitals in assessing the appropriateness of antimicrobial prescribing practices. Since its release in 2013, the NAPS has been adopted by all hospital types within Australia, including public and private facilities, and supports them in meeting the national standards for accreditation. Hospitals can generate real-time reports to assist with local antimicrobial stewardship (AMS) activities and interventions. De-identified aggregate data from the NAPS are also submitted to the Antimicrobial Use and Resistance in Australia surveillance system, for national reporting purposes, and to strengthen national AMS strategies. With the successful implementation of the programme within Australia, the NAPS has now been adopted by countries with both well-resourced and resource-limited healthcare systems. We provide here a narrative review describing the experience of users utilizing the NAPS programme in Canada, Malaysia and Bhutan. We highlight the key barriers and facilitators to implementation and demonstrate that the NAPS methodology is feasible, generalizable and translatable to various settings and able to assist in initiatives to optimize the use of antimicrobials.
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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 | 0.044 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".