Support to scale antibiotic stewardship in long-term care homes: how much is enough?
Bibliographic record
Abstract
As evidence demonstrating the positive impact of antibiotic stewardship interventions grows, there is an urgent need to understand how these efforts can be replicated in other settings (‘spread’) and how infrastructure can be developed to support broader implementation across large systems of care (‘scale’).1 2 In addition to ensuring that individual patients are protected from adverse effects of unnecessary antibiotics, there is a societal imperative to spread and scale stewardship such that it reaches large numbers of people, as misuse of antibiotics has a ripple effect across populations through the emergence of resistant infections. Identifying strategies to move stewardship beyond the controlled, well-resourced world of research to the real world will ensure that the benefit of investment in research is maximised while population harms from antibiotic overuse are minimised.3 In this issue of BMJ Quality and Safety , Chambers and colleagues report the results of a controlled before-and-after study, accompanied by a process evaluation, assessing the impact of virtual learning collaboratives to scale an antibiotic stewardship programme that had previously been demonstrated to be effective in long-term care homes (LTCHs) across Ontario.4 Public Health Ontario (PHO), an arm’s length governmental body that provides scientific expertise to support healthcare in the province, previously developed the Urinary Tract Infection (UTI) Program to reduce inappropriate urine culturing and unnecessary antibiotic prescribing for asymptomatic bacteriuria in non-catheterised residents of LTCHs. The multimodal UTI Program consists of written guidance for LTCHs about how to adopt best practices through a list of specific implementation strategies (eg, readiness strategies, education, monitoring) and a suite of tools to support these efforts (eg, fact sheets, posters, communication material for patients and families, process surveillance forms, assessment algorithm for UTIs).5 After demonstrating effectiveness at reducing urine culturing and antibiotic use in a small pilot of 10 …
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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 | 0.053 | 0.282 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".