Utility of healthcare-worker-targeted antimicrobial stewardship interventions in hospitals of low- and lower-middle-income countries: a scoping review of systematic reviews
Bibliographic record
Abstract
BACKGROUND: Antimicrobial stewardship (AMS) initiatives in hospitals often include the implementation of clustered intervention components to improve the surveillance and targeting of antibiotics. However, impacts of the individual components of AMS interventions are not well known, especially in low- and lower-middle-income countries (LLMICs). OBJECTIVE: A scoping review was conducted to summarize evidence from systematic reviews (SRs) on the impact of common hospital-implemented healthcare-worker-targeted components of AMS interventions that may be appropriate for LLMICs. METHODS: Major databases were searched systematically for SRs of AMS interventions that were evaluated in hospitals. For SRs to be eligible, they had to report on at least one intervention that could be categorized according to the Effective Practice and Organisation of Care taxonomy. Clinical and process outcomes were considered. Primary studies from LLMICs were consulted for additional information. RESULTS: Eighteen SRs of the evaluation of intervention components met the inclusion criteria. The evidence shows that audit and feedback, and clinical practice guidelines improved several clinical and process outcomes in hospitals. An unintended consequence of interventions was an increase in the use of antibiotics. There was a cumulative total of 547 unique studies, but only 2% (N=12) were conducted in hospitals in LLMICs. Two studies in LLMICs reported that guidelines and educational meetings were effective in hospitals. CONCLUSION: Evidence from high- and upper-middle-income countries suggests that audit and feedback, and clinical practice guidelines have the potential to improve various clinical and process outcomes in hospitals. The lack of evidence in LLMIC settings prevents firm conclusions from being drawn, and highlights the need for further research.
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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 | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".