Evaluating and prioritizing antimicrobial stewardship programs for nursing homes: A modified Delphi panel
Bibliographic record
Abstract
BACKGROUND: Antibiotic use in nursing homes is often inappropriate, in terms of overuse and misuse, and it can be linked to adverse events and antimicrobial resistance. Antimicrobial stewardship programs (ASPs) can optimize antibiotic use by minimizing unnecessary prescriptions, treatment cost, and the overall spread of antimicrobial resistance. Nursing home providers and residents are candidates for ASP implementation, yet guidelines for implementation are limited. OBJECTIVE: To support nursing home providers with the selection and adoption of ASP interventions. DESIGN AND SETTING: A multiphase modified Delphi method to assess 15 ASP interventions across criteria addressing scientific merit, feasibility, impact, accountability, and importance. This study included surveys supplemented with a 1-day consensus meeting. PARTICIPANTS: A 16-member multidisciplinary panel of experts and resident representatives. RESULTS: From highest to lowest, 6 interventions were prioritized by the panel: (1) guidelines for empiric prescribing, (2) audit and feedback, (3) communication tools, (4) short-course antibiotic therapy, (5) scheduled antibiotic reassessment, and (6) clinical decision support systems. Several interventions were not endorsed: antibiograms, educational interventions, formulary review, and automatic substitution. A lack of nursing home resources was noted, which could impede multifaceted interventions. CONCLUSIONS: Nursing home providers should consider 6 key interventions for ASPs. Such interventions may be feasible for nursing home settings and impactful for improving antibiotic use; however, scientific merit supporting each is variable. A multifaceted approach may be necessary for long-term improvement but difficult to implement.
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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 | 0.107 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".