Effectiveness of safety-engineered devices in reducing sharp object injuries
Bibliographic record
Abstract
BACKGROUND: Sharps injuries remain a common factor in occupational exposure of healthcare workers to blood-borne viruses. The extent to which the introduction of safety-engineered devices has been effective in reducing such injuries among healthcare workers is unclear. AIMS: To investigate the incidence of sharp object injury among healthcare workers in the Capital Health Region of Alberta, Canada and to determine the effectiveness of the introduction of safety- engineered devices in preventing these. METHODS: All reports of sharp object injuries to Capital Region Workplace Health and Safety offices from healthcare workers 2003-10 were analysed. Rates of sharp object injury were compared before (2006), during (2007-08) and after (2009-10) the introduction of safety-engineered devices, adjusting for other potential risk factors using Poisson regression and log-linear models. RESULTS: Between 2003 and 2010, a total of 4707 sharp object injuries were reported from 15 healthcare facilities. The sharp object injury rate per 1000 full-time equivalent employees per year declined from 35 before the introduction period to 30 during the introduction period (rate ratio [RR]: 0.88, 95% confidence interval [CI]: 0.78, 0.99) among most healthcare workers, but then rebounded again slightly after the intervention. Physician risks showed little change during the period of introduction (odds ratio [OR]: 0.99, 95% CI: 0.85, 1.14) but decreased significantly after the intervention (OR: 0.83, 95% CI: 0.71, 0.97). CONCLUSIONS: The introduction of safety-engineered devices was associated with a modest reduction in reported sharp object injuries but this appeared to be relatively short-lived for most workers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".