Effect of an Educational Intervention on Optimizing Antibiotic Prescribing in Long‐Term Care Facilities
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To assess the effect of an educational intervention aimed at optimizing antibiotic prescribing in long-term care (LTC) facilities. DESIGN: Cluster randomized, controlled trial. SETTING: Eight public LTC facilities in the Montreal area. PARTICIPANTS: Thirty-six physicians. INTERVENTION: The educational intervention consisted of mailing an antibiotic guide to physicians along with their antibiotic prescribing profile covering the previous 3 months. Targeted infections were urinary tract, lower respiratory tract, skin and soft tissues, and septicemia of unknown origin. In the prescribing profile, each antibiotic was classified as adherent or nonadherent to the guide. Physicians in the experimental group received the intervention twice, 4 months apart, whereas physicians in the control group provided usual care. MEASUREMENTS: Data on antibiotic prescriptions were collected over four 3-month periods: preintervention, postintervention I, postintervention II, and follow-up. A generalized estimating equation (GEE) model was used to compare the proportion of nonadherent antibiotic prescriptions of the experimental and control groups. RESULTS: By the end of the study, nonadherent antibiotic prescriptions decreased by 20.5% in the experimental group, compared with 5.1% in the control group. Based on the GEE model, during postintervention II, physicians in the experimental group were 64% less likely to prescribe nonadherent antibiotics than those in the control group (odds ratio=0.36, 95% confidence interval=0.18-0.73). CONCLUSION: An educational intervention combining an antibiotic guide and a prescribing profile was effective in decreasing nonadherent antibiotic prescriptions. Repetition of the intervention at regular intervals may be necessary to maintain its effectiveness.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it