War on drug resistance: Policy interventions to tackle antibiotic misuse in Canada
Bibliographic record
Abstract
Antimicrobial resistance is a growing threat in Canada with profound implications for public health and wellbeing. Widespread misuse of antibiotics has led to increasing numbers of drug-resistant “superbugs” capable of causing serious and potentially untreatable infections. Addressing antibiotic misuse is crucial in order to curb antimicrobial resistance, but there is a lack of coordinated policy action across the country. Furthermore, research on the predictors of antibiotic misuse in Canada is sparse, which hinders policy makers’ ability to develop targeted interventions. This study analyzes national survey data to shed light on the extent of antibiotic misuse in Canada, including uncovering socio-demographic predictors of public misuse. The findings are used to inform proposed policy recommendations that aim to reduce antibiotic misuse in order to better position Canada to tackle antimicrobial resistance in the years ahead.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".