Study reporting quality among interventions to reduce antibiotic use is a barrier to evidence-informed policymaking on antimicrobial resistance: systematic review
Bibliographic record
Abstract
BACKGROUND: Countries are currently seeking evidence-informed policy options to address antimicrobial resistance (AMR). While rigorous evaluations of AMR interventions are the ideal, they are far from the current reality. Additionally, poor reporting and documentation of AMR interventions impede efforts to use evidence to inform future evaluations and policy interventions. OBJECTIVES: To critically evaluate reporting quality gaps in AMR intervention research. METHODS: To evaluate the reporting quality of studies, we conducted a descriptive synthesis and comparative analysis of studies that were included in a recent systematic review of government policy interventions aiming to reduce human antimicrobial use. Reporting quality was assessed using the SQUIRE 2.0 checklist of 18 items for reporting system-level interventions to improve healthcare. Two reviewers independently applied the checklist to 66 studies identified in the systematic review. RESULTS: None of the studies included complete information on all 18 SQUIRE items (median score = 10, IQR = 8-11). Reporting quality varied across SQUIRE items, with 3% to 100% of studies reporting the recommended information for each SQUIRE item. Only 20% of studies reported the elements of the intervention in sufficient detail for replication and only 24% reported the mechanism through which the intervention was expected to work. CONCLUSIONS: Gaps in the reporting of impact evaluations pose challenges for interpreting and replicating study results. Failure to improve reporting practice of policy evaluations is likely to impede efforts to tackle the growing health, social and economic threats posed by AMR.
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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 | 0.358 | 0.759 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".