Virtual learning collaboratives to improve urine culturing and antibiotic prescribing in long-term care: controlled before-and-after study
Bibliographic record
Abstract
BACKGROUND: Urine culturing practices are highly variable in long-term care and contribute to overprescribing of antibiotics for presumed urinary tract infections. The purpose of this study was to evaluate the use of virtual learning collaboratives to support long-term care homes in implementing a quality improvement programme focused on reducing unnecessary urine culturing and antibiotic overprescribing. METHODS: Over a 4-month period (May 2018-August 2018), 45 long-term care homes were self-selected from five regions to participate in virtual learning collaborative sessions, which provided an orientation to a quality improvement programme and guidance for implementation. A process evaluation complemented the use of a controlled before-and-after study with a propensity score matched control group (n=127) and a difference-in-difference analysis. Primary outcomes included rates of urine cultures performed and urinary antibiotic prescriptions. Secondary outcomes included rates of emergency department visits, hospital admission and mortality. An 18-month baseline period was compared with a 16-month postimplementation period with the use of administrative data sources. RESULTS: Rates of urine culturing and urinary antibiotic prescriptions per 1000 resident days decreased significantly more among long-term care homes that participated in learning collaboratives compared with matched controls (differential reductions of 19% and 13%, respectively, p<0.0001). There was no statistically significant changes to rates of emergency department visits, hospital admissions or mortality. These outcomes were observed with moderate adherence to the programme model. CONCLUSIONS: Rates of urine culturing and urinary antibiotic prescriptions declined among long-term care homes that participated in a virtual learning collaborative to support implementation of a quality improvement programme. The results of this study have refined a model to scale this programme in long-term care.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".