Evaluation of an OPEN Stewardship generated feedback intervention to improve antibiotic prescribing among primary care veterinarians in Ontario, Canada and Israel: protocol for evaluating usability and an interrupted time-series analysis
Bibliographic record
Abstract
INTRODUCTION: Antimicrobial resistance (AMR) impacts the health and well-being of animals, affects animal owners both socially and economically, and contributes to AMR at the human and environmental interface. The overuse and/or inappropriate use of antibiotics in animals has been identified as one of the most important drivers of the development of AMR in animals. Effective antibiotic stewardship interventions such as feedback can be adopted in veterinary practices to improve antibiotic prescribing. However, the provision of dedicated financial and technical resources to implement such systems are challenging. The newly developed web-based Online Platform for Expanding Antibiotic Stewardship (OPEN Stewardship) platform aims to automate the generation of feedback reports and facilitate wider adoption of antibiotic stewardship. This paper describes a protocol to evaluate the usability and usefulness of a feedback intervention among veterinarians and assess its impact on individual antibiotic prescribing. METHODS AND ANALYSIS: Approximately 80 veterinarians from Ontario, Canada and 60 veterinarians from Israel will be voluntarily enrolled in a controlled interrupted time-series study and their monthly antibiotic prescribing data accessed. The study intervention consists of targeted feedback reports generated using the OPEN Stewardship platform. After a 3-month preintervention period, a cohort of veterinarians (treatment cohort, n=120) will receive three feedback reports over the course of 6 months while the remainder of the veterinarians (n=20) will be the control cohort. A survey will be administered among the treatment cohort after each feedback cycle to assess the usability and usefulness of various elements of the feedback report. A multilevel negative-binomial regression analysis of the preintervention and postintervention antibiotic prescribing of the treatment cohort will be performed to evaluate the impact of the intervention. ETHICS AND DISSEMINATION: Research ethics board approval was obtained at each participating site prior to the recruitment of the veterinarians. The study findings will be disseminated through open-access scientific publications, stakeholder networks and national/international meetings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.040 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".