Incidence and characteristics of needlestick injuries among medical trainees at a community teaching hospital: A cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: This field study aimed to determine the incidence and distribution of needlestick injuries among medical trainees at a community teaching hospital in Toronto, Canada. METHODS: The study was performed during the 2013-2015 academic years at Toronto East General Hospital (TEGH), a University of Toronto-affiliated community-teaching hospital during the 2013-2015 academic years. Eight-hundred and forty trainees, including medical students, residents, and post-graduate fellows, were identified and invited via email to participate in an anonymous online fluidsurveys.com survey of 16 qualitative and quantitative questions. RESULTS: Three-hundred and fifty trainees responded (42% response rate). Eighty-eight (25%) respondents reported experiencing at least one injury at TEGH. In total, our survey identified 195 total injuries. Surgical trainees were significantly more likely to incur injuries than non-surgical trainees (IRR = 3.03, 95% CI 1.80-5.10). Orthopaedic surgery trainees had the highest risk of a needlestick injury, being over 12 times more likely to be injured than emergency medicine trainees (IRR = 12.4, 95% CI 2.11-72.32). Only 28 of the 88 most recent needlestick injuries were reported to occupational health. Trainees reported a perception of insignificant risk, lack of resources and support for reporting, and injury stigmatization as reasons for not reporting needlestick injuries. CONCLUSIONS: Needlestick injuries were a common underreported risk to medical trainees at TEGH. Future research should investigate strategies to reduce injury and improve reporting among the high-risk and reporting-averse trainees.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".