Opportunities and barriers for providing HIV testing through community health centers in mainland China: a nationwide cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Primary care may be an avenue to increase coverage of HIV testing but it is unclear what challenges primary healthcare professionals in low- and middle-income countries face. We describe the HIV testing practices in community health centres (CHCs) and explore the staff's attitude towards further development of HIV testing services at the primary care level in China. METHODS: We conducted a national, cross-sectional survey using a stratified random sample of CHCs in 20 cities in 2015. Questionnaires were completed by primary care doctors and nurses in CHCs, and included questions regarding their demographics, clinical experience and their views on the facilitators and barriers to offering HIV testing in their CHC. Multivariate logistic regression was conducted to examine the association between staff who would offer HIV testing and their sociodemographic characteristics. RESULTS: A total of 3580 staff from 158 CHCs participated. Despite the majority (81%) agreeing that HIV testing was an important part of healthcare, only 25% would provide HIV testing when requested by a patient. The majority (71%) were concerned about reimbursement, and half (47%) cited lack of training as a major barrier. Almost half (44%) believed that treating people belonging to high-risk populations would scare other patients away, and 6% openly expressed their dislike of people belonging to high-risk populations. Staff who would offer HIV testing were younger (adjusted odds ratio (aOR) 0.97 per year increase in age, 95% confidence interval (CI):0.97-0.98); trained as a doctor compared to a nurse (aOR 1.79, 95%CI:1.46-2.15); held a bachelor degree or above (aOR 1.34, 95%CI:1.11-1.62); and had previous HIV training (aOR 1.55, 95%CI:1.27-1.89). CONCLUSIONS: Improving HIV training of CHC staff, including addressing stigmatizing attitudes, and improving financial reimbursement may help increase HIV testing coverage in China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".