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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".