Increasing sharp safety device use in healthcare: A semi-structured interview study
Bibliographic record
Abstract
The use of sharp safety devices in healthcare is considered the most important means of preventing occupational percutaneous injuries and has been mandated for use in most hospitals in industrialized countries including in Canada. However, clinical personnel's perceptions on the use of safety devices needs further characterization to improve compliance. This study's objective was to identify healthcare provider perspectives on different aspects of sharp safety device use and on how use could be increased. Using a constant comparison approach, data from semi-structured interviews with 39 nurses, physicians and phlebotomists providing direct patient care, and six nurses acting as the interface between clinical personnel and purchasing departments, were analyzed. Study participants were from three of the six health authorities in British Columbia. The four major categories that emerged from the data were: selection processes; identification and replacement; training; and multi-level barriers and facilitators. Findings highlighted the importance of including personnel regularly using safety devices at each stage of their selection including when they are being considered for replacement with superior devices, as well as the need for appropriate initial and refresher training, and how practices at the hospital, ward and individual level facilitate safety device use.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".