Use of Gloves and Reduction of Risk of Injury Caused by Needles or Sharp Medical Devices in Healthcare Workers: Results from a Case-Crossover Study
Bibliographic record
Abstract
OBJECTIVE: Standard precautions are advocated for reducing the number of injuries caused by needles and sharp medical devices ("sharps injuries"), but the effectiveness of gloves in preventing such injuries has not been established. We evaluated factors associated with gloving practices and identified associations between gloving practices and sharps-injury risk. DESIGN: Usual-frequency case-crossover study. SETTING: Thirteen medical centers in the United States and Canada. PARTICIPANTS: Six hundred thirty-six healthcare workers who presented to employee health clinics after sharps injury. METHODS: Structured telephone questionnaires were administered to assess usual behaviors and circumstances at the time of injury. RESULTS: Of 636 injured healthcare workers, 195 were scrubbed in an operating room or procedure suite when injured, and 441 were injured elsewhere. Nonscrubbed individuals were more commonly gloved when treating patients who were perceived to have a high risk of human immunodeficiency virus, hepatitis B virus, or hepatitis C virus infection than when treating other patients (adjusted odds ratio [aOR], 2.53 [95% confidence interval {CI}, 1.30-4.91]). Nurses (aOR, 0.11 [95% CI, 0.04-0.32]) and other employees (aOR, 0.24 [95% CI, 0.07-0.77]) were less commonly gloved at injury than were physicians and physician trainees. Gloves reduced injury risk in case-crossover analyses (incidence rate ratio [IRR], 0.33 [95% CI, 0.22-0.50]). In scrubbed individuals, involvement in an orthopedic procedure was associated with double gloving at injury (aOR, 13.7 [95% CI, 4.55-41.3]); this gloving practice was associated with decreased injury risk (IRR, 0.20 [95% CI, 0.10-0.42]). CONCLUSIONS: Although the use of gloves reduces the risk of sharps injuries in health care, use among healthcare workers is inconsistent and may be influenced by risk perception and healthcare culture. Glove use should be emphasized as a key element of multimodal sharps-injury reduction programs.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".