244 Implementing changes to protect workers from exposures to hazardous drugs
Bibliographic record
Abstract
Abstract Hazardous drugs (HD) present a complex exposure problem within healthcare facilities whereby nursing, pharmacy and other staff are chronically exposed to low-levels of HDs via contaminated surfaces throughout pharmacies and clinical settings. Many of these drugs are designated as ALARA substances (reproductive toxins and/or carcinogens); exposures to unknown mixtures or cleaning agent reaction by-products may also occur but have been poorly characterized. There is a lack of health-based surface-OELs against which to benchmark exposures. Despite increased awareness of HD exposures and promotion of safe handling measures, surface levels of HD have plateaued over the last decade. New, collaborative approaches are needed to reduce environmental contamination of HD that contribute to exposures among healthcare workers and staff. We introduce a new approach involving a broad, collaborative, system-wide program based on social dialogue models that brings all parties (e.g., employees/unions, employers, scientists and regulators) together to work on a common goal (e.g., HD exposure reduction) in a collaborative manner that is evidence-based (e.g., surveillance data) that have been successfully implemented in Europe, and an embedded quantitative exposure assessment strategy that uses “hygienic guidance values” (HGV) in the absence of surface OELS. This HGV-based surveillance strategy provides key operational details to the user and also provides target performance levels (through HGV rather than OELs). This approach offers support for a quantified ALARA response, as an HGV approach can encourage measured and continual improvement (i.e., exposure reductions) over time, and centers on a continuous improvement paradigm.
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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 | 0.031 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".