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Record W2510446235 · doi:10.5539/gjhs.v9n4p156

Occupational Exposure to Positive Blood and Body Fluids among Health Care Workers in a Chinese University Hospital: A Three Years Retrospective Study

2016· article· en· W2510446235 on OpenAlexvenueno aff
Xiubin Tao, Hui Peng, Li-Hua Qian, Yan Li, Qun Wu, Jingjing Ruan, Dong-Zhen Cai

Bibliographic record

VenueGlobal Journal of Health Science · 2016
Typearticle
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIncidence (geometry)EpidemiologyHepatitis BRetrospective cohort studyOccupational safety and healthOccupational exposureHepatitis CHealth careHepatitis B virusHuman immunodeficiency virus (HIV)Environmental healthEmergency medicineFamily medicineInternal medicineImmunologyPathologyVirus

Abstract

fetched live from OpenAlex

<p>Health care workers (HCWs) are exposed to blood and body fluids (BBF) due to occupational accidents. However, few studies have investigated the prevalence of occupational exposure in Chinese HCWs thus far. There is a clearly a critical need to characterize its epidemiology more fully in China so that effective prevention programs can be implemented. We conducted a retrospective study at a university hospital in China, giving an epidemiological analysis on these exposed HCWs whose pathogens of BBF from patients were positive [human immunodeficiency virus (HIV) / hepatitis B (HBV) / hepatitis C (HCV)]. From July 1st 2011 to June 30th 2014, a total of 155 occupational exposures to positive BBF were reported, with an incidence of 16.64 (/1000 person-years). Percutaneous injuries were the most common type of exposure episodes (89.03%). The most common type of exposed blood-borne pathogens was HBV (83.87%), and the majority of the respondents were nursing students, with an incidence of 34.22 (/1000 person-years). More effective preventive strategies on HCWs’ BBF occupational exposure should be implemented in China, especially for nursing students.</p>

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)0.002
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 score0.009
Threshold uncertainty score0.781

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.320
Teacher spread0.311 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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