Barriers and facilitators for the safe handling of antineoplastic drugs
Bibliographic record
Abstract
INTRODUCTION: Antineoplastic drugs are widely used in the treatment of cancer. However, some are known carcinogens and reproductive toxins, and incidental low-level exposure to workers is a health concern. CAREX Canada estimated that approximately 75,000 Canadians are exposed to antineoplastic drugs in workplace settings. While policies and guidelines on safe handling of antineoplastic drugs are available, evidence suggests that compliance is low. In this paper, we identify barriers and facilitators for safe handling of antineoplastic drugs in workplace settings. METHODS: We utilized a unique method to study public policy which involved compiling policy levers, developing a logic model, conducting a literature review, and contextualizing data through a deliberative process with stakeholders to explore in-depth contextual factors and experiences for the safe handling of antineoplastic drugs. RESULTS: The most common barriers identified in the literature were: poor training (46%), poor safety culture (41%), and inconsistent policies (36%). The most common facilitators were: adequate safety training (41%), leadership support (23%), and consistent policies (21%). Several of these factors are intertwined and while this means one barrier can cause other barriers, it also allows healthcare employers to mitigate these barriers by implementing small but meaningful changes in the workplace. CONCLUSION: The combination of barriers and facilitators identified in our review highlight the importance of creating work environments where safety is a priority for the safe handling of antineoplastic drugs. The results of this study will assist policy makers and managers in identifying gaps and enhancing strategies that reduce occupational exposure to antineoplastic 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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".