Antibiotic Prescribing in 4 Assisted-Living Communities: Incidence and Potential for Improvement
Bibliographic record
Abstract
OBJECTIVE: To describe the prevalence, characteristics, and appropriateness of systemic antibiotic use in assisted living (AL) and to conduct a preliminary quality improvement intervention trial to reduce inappropriate prescribing. DESIGN: Pre-post study, with a 13-month intervention period. SETTING: Four AL communities. PARTICIPANTS: All prescribers, all AL staff who communicate with prescribers, and all patients who had an infection during the baseline and intervention periods. INTERVENTION: A standardized form for AL staff, an online education course and 5 practice briefs for prescribers, and monthly quality improvement meetings with AL staff. MEASUREMENTS: Monthly inventory of all systemic antibiotic prescriptions; interviews with the prescriber, AL staff member, closest family member, and patient (when capable) regarding 85 antibiotic prescribing episodes (30 baseline, 55 intervention), with data review by an expert panel to determine prescribing appropriateness. RESULTS: The mean number of systemic antibiotic prescriptions was 3.44 per 1,000 resident-days at baseline and 3.37 during the intervention, a nonsignificant change (P = .30). Few prescribers participated in online training. AL staff use of the standardized form gradually increased during the program. The proportion of prescriptions rated as probably inappropriate was 26% at baseline and 15% during the intervention, a nonsignificant trend (P = .25). Drug selection was largely appropriate during both time periods. CONCLUSIONS: AL antibiotic prescribing rates appear to be approximately one-half those seen in nursing homes, with up to a quarter being potentially inappropriate. Interventions to improve prescribing must reach all physicians and staff and most likely will require long time periods to have the optimal effect.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".