Using behavioural science to improve antibiotic stewardship in Canadian long-term care homes: Protocol for a multi-center cluster randomized quality improvement study
Bibliographic record
Abstract
Background: Antimicrobial resistance (AMR) is associated with significant human and financial costs, particularly among vulnerable populations like older adults living in long-term care homes (LTCHs). Urinary tract infection (UTI) is the leading indication for antibiotic use in this population, with some estimates suggesting that up to 70% of these prescriptions may be avoidable. Objective: The purpose of this study is to develop and test novel behavioural science-informed antimicrobial stewardship (AMS) quality improvement strategies in Canadian LTCHs, which aim to decrease unnecessary testing and treatment for residents who lack the minimum clinical signs and symptoms of UTI. Intervention: The quality improvement strategy is a two-pronged approach that includes 1) targeted education for essential care providers (family and friends of LTCH residents) about UTI and benefits of AMS, which strives to outline a positive role for this group in UTI management, and 2) monthly feedback to LTCH staff on their facility's urine culture ordering rates. Outcomes: The protocol was piloted in a single LTCH; a process evaluation of the pilot implementation served to refine the research protocol, which is being implemented in eight LTCHs across Canada using an eight-month stepped wedge randomized cluster design. Conclusion: This protocol represents a behavioural science-informed intervention to improve AMS across LTCHs. If successful, this model of care could be scalable across Canadian LTCHs, offering an inclusive approach that aims to empower clinicians, non-regulated healthcare staff, residents and their family and friends to improve health outcomes as antibiotic stewards.
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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 | 0.066 | 0.061 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".