Impact of a Multipronged Education Strategy on Antibiotic Prescribing in Quebec, Canada
Bibliographic record
Abstract
BACKGROUND: Antibiotic overuse and resistance have become a major threat in the last 2 decades. Many programs tried to optimize antibiotic consumption in the inpatient setting, but the outpatient environment that represents the bulk of antibiotic use has been challenging. Following a significant rise of Clostridium difficile infections, all the health care stakeholders in the province of Quebec, Canada initiated a global education program targeting physicians and pharmacists. METHODS: A bundle approach was used; 11 user-friendly guidelines were produced by a group of experts and sent to all physicians and pharmacists in Quebec in January 2005. Downloadable versions of guidelines were posted on a dedicated Web site. They were promoted by professional organizations, universities, and experts during educational events, and there was strong acceptance by the pharmaceutical industry with a willingness to follow the recommendations in their marketing. The Intercontinental Medical Statistics (IMS) database was used to analyze and compare Quebec's total outpatient prescriptions per 1000 inhabitants with those in the other Canadian provinces for 2 time periods: preintervention (January 2003 to December 2004), and postintervention (February 2005 to December 2007). RESULTS: In 2004, antibiotic consumption per capita was 23.3% higher in Canada generally than in Quebec. After the guidelines dissemination, the gap between Quebec and the other Canadian provinces increased by 4.1 prescriptions/1000 inhabitants (P = .0002), and the trend persisted 36 months later. Antibiotic costs fell $134.5/1000 inhabitants in Quebec compared with the rest of Canada (P = .054). CONCLUSIONS: The implementation of guidelines significantly reduced antibiotic prescriptions in Quebec compared with the rest of the country, and there was a strong trend toward significant cost reduction.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".