National survey of safe handling of hazardous drugs in hospital settings: Use of an innovative approach
Bibliographic record
Abstract
INTRODUCTION: Workers can reduce their risk of exposure to hazardous drugs by following safe handling guidelines. Healthcare centers need to dedicate time and resources in order to implement new safety recommendations. The objective was to present the results of a national survey about the safe handling of hazardous drugs in healthcare centers. METHODS: Quebec healthcare centers performed an auto-evaluation to the newly updated safe handling guidelines in 2021. Centers rated each criterion as compliant or non-compliant. The guidelines tailored recommendations according to three categories of hazards: G1, consisting mostly of carcinogenic drugs; G2, other hazardous drugs; and G3, those with reproductive toxicity. The questionnaire prompted participants to document their planned corrective measures for non-compliant criteria. RESULTS: Most centers participated (28/29, 97%). The overall compliance was 58% (8761/15,216 criteria). The conformity per theme was hygiene and sanitation (1290/1,878, 69%), laundry (221/367, 60%), pharmacy (2658/4,474, 59%), nursing (3436/6,017, 57%), spills and accidental exposure (353/649, 54%), and general measures (803/1,831, 44%). It was higher for recommendations regarding G1s (4226/6,115, 69%) than for G2s (1626/3557, 46%) and G3s (372/916, 41%). CONCLUSIONS: This project successfully used an innovative approach that combined a national auto-evaluation survey, an actionable report, and the involvement of a community of practice. Centers were able to benchmark their implementation of safe handling guidelines, and community of practices may help in sharing the best practices. The design of the questionnaire helped in targeting corrective measures. More work is needed for safe handling practices that relate to G2 and G3 drugs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".