Evaluation of the Effectiveness of Close-Knit Medical Alliances on the Integration of HIV Prevention and Treatment in County-Level Areas in China: Protocol for a Delphi Study
Bibliographic record
Abstract
Background: Integrated care for patients living with HIV in rural and economically disadvantaged regions remains a critical challenge. This study aims to develop a 3D evaluation index system to assess the effectiveness of integrated medical care and prevention for patients living with HIV in county-level regions of China. The framework encompasses disease prevention, clinical treatment, and the integration of medical services with public health interventions. Objective: This study aims to develop a scientifically validated, 3D evaluation index system for assessing the effectiveness of integrated medical care and prevention for patients living with HIV in county-level regions of China. Methods: A structured Delphi method will be used to establish consensus among experts on key evaluation indicators. The study will begin with focus group discussions guided by the Valentijn Rainbow Model, followed by multiple rounds of Delphi surveys to refine the indicator system. A total of 75 experts will include clinical professionals, public health specialists, health policy researchers, and representatives from nongovernmental organizations. Experts will participate in iterative rounds, ranking and validating indicators using statistical methods such as the analytic hierarchy process and Kendall W coefficient to assess consensus. Results: The Delphi study was initiated in April 2025, with 3 rounds of questionnaires distributed between May and September 2025. Data analysis is ongoing, and the finalized county-level HIV integration index system is expected to be published by spring 2026. The study is projected to be completed by January 2026. Conclusions: The developed evaluation system will offer a comprehensive and scientifically validated tool for assessing health care outcomes in resource-constrained settings, contributing to policy improvements for HIV service integration in China and other low-resource settings globally.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.084 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".