Perceptions of antimicrobial stewardship: identifying drivers and barriers across various professions in Canada utilizing a one health approach
Bibliographic record
Abstract
Introduction: As antimicrobial resistance (AMR) represents a substantial threat to the efficacy of available antimicrobial options, it is important to understand how to implement effective and practical mitigation efforts, including antimicrobial stewardship (AMS), across human, animal, and environmental sectors. Methods: = 81) were largely from the veterinary (75%) or human (19%) health sectors. Qualitative data were analyzed in NVivo using template analysis whereas quantitative data were analyzed in STATA using Kruskall-Wallis tests. The questionnaire asked respondents about their perceptions of AMS, as well as the perceived barriers and drivers of AMS efforts. Results: Perceptions of what AMS meant to the respondents personally and their profession as a whole were grouped into 3 main themes: 1) AMS strategies or considerations in antimicrobial prescribing and use; 2) responsibility to maintain health and preserve antimicrobial effectiveness; and 3) reducing antimicrobial use (AMU) as a goal of AMS efforts. Identified AMS barriers had 3 main themes: 1) lack of various prescribing and AMU support mechanisms; 2) shift in prescriber attitudes to drive change; and 3) stronger economic considerations to support shifting prescribing practices. Drivers of AMS had the following themes: 1) leadership to guide change; 2) education to support optimizing AMU; and 3) research to identify best practices and opportunities for action. Across all questions, 2 cross-cutting themes emerged: 1) a One Health understanding of AMS; and 2) blame placed on others for a lack of AMS success. Conclusion: Overall, sector-specific, but particularly cross-sectoral AMS drivers and barriers were identified, highlighting the importance of a One Health approach in AMR research and mitigation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".