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Record W4388544735 · doi:10.2196/39472

Hepatitis C Knowledge and Self-Reported Testing Behavior in the General Population in China: Online Cross-Sectional Survey

2023· article· en· W4388544735 on OpenAlexvenueno aff
Yin Liu, Juan Su, Xiaoyang Wang, Huifang Xu, Hong Wang, Ruihua Kang, Liyang Zheng, Yi-xian Wang, Chunya Liu, Yiping Jing, Shaokai Zhang

Bibliographic record

VenueJMIR Public Health and Surveillance · 2023
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsnot available
Fundersnot available
KeywordsCross-sectional studyPopulationEnvironmental healthMedicineVirologyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: The World Health Organization has proposed a worldwide target of eliminating hepatitis C virus (HCV) by 2030. A better understanding of HCV, testing behaviors, and associated factors in the general population is essential. OBJECTIVE: This study aimed to assess HCV knowledge, self-reported HCV testing behavior, and willingness to undergo HCV screening in the general Chinese population. METHODS: A cross-sectional online survey of the general Chinese population aged ≥15 years was conducted from November 2021 to May 2023. Participant characteristics were assessed based on their knowledge level and uptake of HCV testing. Participants ever having heard of HCV were recognized as being aware of HCV and asked additional HCV knowledge questions using a brief, validated 9-item scale. Participants with 0-3 points and who were unaware of HCV were categorized as having poor knowledge, and those with 4-6 points and 7 points were categorized as having fair and good knowledge, respectively. Participant uptake of HCV testing, testing results, reasons for undergoing or not undergoing HCV testing, and willingness to undergo HCV screening were collected through self-reports. Ordinal and binary logistic regression analyses were used to assess factors associated with the HCV knowledge level and the uptake of HCV testing, respectively. RESULTS: A total of 1491 valid participants' questionnaires were included. Of these, 714 (47.6%) participants were aware of HCV. The proportion of participants with poor, fair, and good HCV knowledge was 63.4% (945/1491), 9.3% (139/1491), and 27.3% (407/1491), respectively. A total of 465 (31.2%) participants reported ever undergoing HCV testing, and 4 (0.9%) were anti-HCV antibody positive. Most participants were tested for HCV following blood donation (353/465, 75.9%). The most common reasons for not undergoing HCV screening were a lack of HCV awareness (665/1026, 64.8%), followed by a low self-perceived risk of infection (176/1026, 17.2%). Of 1026 participants who had never undergone HCV testing, 937 (91.3%) were willing to undergo HCV screening if universal screening was provided at no cost. The HCV knowledge level was positively associated with the HCV testing rate. Participants who were less educated, lived in rural areas, resided in West China, and were currently alcohol drinkers had lower HCV knowledge and reduced odds of having undergone HCV testing. In contrast, participants with a blood donation history and a family history of hepatitis B virus or HCV infection had higher HCV knowledge and increased odds of prior testing. Participants aged ≥60 years had lower knowledge, and women had reduced odds of having undergone previous HCV testing. CONCLUSIONS: The general population of China has low HCV knowledge and testing rate. There is an urgent need for enhanced HCV awareness and scaled-up HCV screening and treatment. Individuals who are less well educated, reside in less-developed areas, currently drink alcohol, and are female should be prioritized for health education and interventions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.130
GPT teacher head0.438
Teacher spread0.308 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2023
Admission routes1
Has abstractyes

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