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Record W4413012764 · doi:10.2196/69832

Perceptions, Barriers, and Facilitators of Provider-Initiated and Voluntary HIV Testing and Counseling Among Health Care Workers: Protocol for a Multicenter Cross-Sectional Study

2025· article· en· W4413012764 on OpenAlexvenueno aff
Bingyi Wang, Leiwen Fu, Cailing Ao, Shilan Xie, Zhen Lu, Yong Lü, Yan Li, Xiaobing Fu, Huihong Deng

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsVoluntary counseling and testingFamily medicineHuman immunodeficiency virus (HIV)Cross-sectional studyHealth careMedicineProtocol (science)PsychologyNursingAlternative medicineHealth servicesEnvironmental healthHealth facilityPopulation

Abstract

fetched live from OpenAlex

Background: HIV testing and counseling interventions have been pivotal in efforts to curb the HIV epidemic, with diverse delivery models implemented globally. However, existing studies primarily focus on individual perspectives, with limited attention given to the essential role of health care workers in the effective implementation of voluntary counseling and testing (VCT) and provider-initiated testing and counseling (PITC) services in China. Objective: This protocol describes the design of the Provider-initiated Views on PITC and VCT Study (PIVOT Study), which aims to assess health care workers' perceptions, barriers, and facilitators related to the implementation of PITC and VCT in Guangdong Province, China. Methods: The PIVOT Study is a multicenter, cross-sectional observational study. Eligible participants are health care workers employed at various health care service institutions, including hospitals, VCT clinics, the Centers for Disease Control and Prevention, and community health centers. We will use a convenience sampling method. Data will be collected via a structured digital questionnaire covering 5 domains: sociodemographic information, general health status, psychosocial characteristics, knowledge related to PITC and VCT, and experiential insights regarding service provision. Descriptive statistics will be used to characterize variable distributions, and multivariable logistic regression models will assess associations between independent and outcome variables. Secondary analyses will explore subgroup differences based on age, years of experience, sex, institution type, and geographical location. A total of approximately 400 health care workers will be recruited. Results: The PIVOT Study proposal was submitted in December 2024 and received funding approval in May 2025, with official project initiation planned for July 2025. Study design and survey instrument revisions were completed between December 2024 and March 2025. A pilot survey was completed from April to May 2025, followed by questionnaire testing and refinement from June to August 2025. Formal data collection was conducted from September to November 2025, with data cleaning and preliminary analyses scheduled from December 2025 to January 2026. Final data analysis and manuscript preparation are planned from February to June 2026. Conclusions: The PIVOT Study will generate important insights into health care workers' perspectives on PITC and VCT service delivery in China. The findings are expected to inform the development of targeted strategies to strengthen HIV testing efforts, particularly among underrepresented populations such as older adults. Study results will be disseminated through peer-reviewed journals and national and international conferences.

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)0.029
metaresearch head score (Gemma)0.018
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: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.036
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.018
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0030.003
Science and technology studies0.0060.002
Scholarly communication0.0020.003
Open science0.0030.002
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0360.006

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 head0.217
GPT teacher head0.574
Teacher spread0.357 · 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
GenreProtocol

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

Citations0
Published2025
Admission routes1
Has abstractyes

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