Barriers to the Adoption of Safety-Engineered Needles Following a Regulatory Standard: Lessons Learned from Three Acute Care Hospitals
Bibliographic record
Abstract
BACKGROUND: A number of jurisdictions have introduced regulation to accelerate the adoption of safety-engineered needles (SENs). This study examined the transition to SENs in three acute care hospitals prior to and following the implementation of a regulatory standard in Ontario. This paper focuses on the ongoing barriers to the prevention of needlestick injuries among healthcare workers. METHODS: Information from document review and 30 informant interviews were used to prepare three case studies detailing each organization's implementation activities and outcomes. RESULTS: All three hospitals responded to the regulatory requirements with integrity and needlestick injuries declined. However, needlestick injuries continued to occur during the activation of safety devices, during procedures and during instrument disposal. The study documented substantial barriers to further progress in needlestick injury prevention. CONCLUSIONS: Healthcare organizations should focus on understanding their site-specific challenges that contribute to ongoing injury risk to better understand issues related to product limitations, practice constraints and the work environment.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".