Antibiotic prescribing and antimicrobial stewardship in long-term care facilities: Past interventions and implementation challenges
Bibliographic record
Abstract
Background: The threat of antimicrobial resistance (AMR) is rising, leading to increased illness, death and healthcare costs. In long-term care facilities (LTCFs), high rates of infection coupled with high antibiotic use create a selective pressure for antimicrobial-resistant organisms that pose a risk to residents and staff as well as surrounding hospitals and communities. Antimicrobial stewardship (AMS) is paramount in the fight against AMR, but its adoption in LTCFs has been limited. Methods: This article summarizes factors influencing antibiotic prescribing decisions in LTCFs and the effectiveness of past AMS interventions that have been put in place in an attempt to support those decisions. The emphasis of this literature review is the Canadian LTCF landscape; however, due to the limited literature in this area, the scope was broadened to include international studies. Results: Prescribing decisions are influenced by the context of the individual patient, their caregivers, the clinical environment, the healthcare system and surrounding culture. Antimicrobial stewardship interventions were found to be successful in LTCFs, though there was considerable heterogeneity in the literature. Conclusion: This article highlights the need for more well-designed studies that explore innovative and multifaceted solutions to AMS in LTCFs.
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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 | 0.023 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".