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Record W4410310068 · doi:10.63564/jha.v14n1p34

Occupational sharps and needlestick injuries among physician residents at an academic health center

2025· article· en· W4410310068 on OpenAlexvenueno aff
Alexei Krainev, Wali Jahangiri, Sofia Villaveces, Victoria Wulsin, Kermit G. Davis, Gordon Lee Gillespie

Bibliographic record

VenueJournal of Hospital Administration · 2025
Typearticle
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsnot available
FundersNational Institute for Occupational Safety and HealthUniversity of Cincinnati
KeywordsCenter (category theory)Needlestick injuryMedicineFamily medicineOccupational exposureNursingMedical emergencyHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

Objective: Occupational sharps and needlestick injuries (SNSI) are a significant and persistent challenge in the U.S. healthcare work environment. With the purpose of better delineating contributing factors for a ubiquitous occupational injury among healthcare workers, we undertook a two-component study of SNSIs among physician residents and nurses at an academic medical center. Methods: Retrospective injury data among nurses (N=58) and medical residents (N=63) were analyzed. A 35-item cross-sectional survey was used to evaluate the prevalence, non-reporting, and contributing factors among physician residents who sustained a SNSI (N=76). Results: Physician residents had a rate of injury that was 11.0 SNSIs/100 medical residents/year compared to nurses at 3.2 SNSIs/100 nurses/year; a rate three-fold higher. Physician residents in neurosurgery, otolaryngology, OB/GYN, and general surgery reported the highest rates of injury. Conclusions: Our results underscore the need for a more comprehensive study to better identify injury drivers specific to the operating room environment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.419

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.366
Teacher spread0.349 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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