Surveillance of antimicrobial use in Québec acute-care hospitals: A survey
Bibliographic record
Abstract
Objectives: In 2011, the Québec Ministry of Health required that hospitals implement surveillance for antimicrobial use in inpatients. This study aims to describe hospitals' available pharmacy data, antimicrobial stewardship programs (ASPs), quantitative antimicrobial surveillance programs (QASPs), and hospitals' motivation to perform surveillance of antimicrobial use. Methods: In 2014, a web-based questionnaire was sent to all acute-care hospitals in the province of Québec for chief pharmacists or pharmacists in charge of antimicrobial use surveillance to complete. Results: The participation rate was 40% (44/109). A pharmacy database describing antimicrobial use was available in 88% of hospitals (86% had aggregated data; 31% had individual-level data). The proportions of hospitals with an ASP or a QASP (or planning to implement one shortly) were 90% and 80%, respectively. Defined daily dose was the most popular indicator used, available in nearly all aggregated pharmacy databases (97%) and in most QASPs (87%). In 80% of hospitals, the respondent supported the implementation of a provincial quantitative surveillance program. The problem participants foresaw was a lack of resources; comparisons between hospitals were seen as both a methodological challenge and useful information. Conclusion: Antimicrobial surveillance programs and the use of defined daily doses were implemented in most participating hospitals, and in higher proportions than in a similar 2006 survey. However, databases were not always readily available, and indicator definitions vary. Most participants favoured a future quantitative provincial surveillance program with appropriate benchmarking.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".