Whole person HIV services: a social science approach
Bibliographic record
Abstract
PURPOSE OF REVIEW: Globally, approximately 38.4 million people who are navigating complex lives, are also living with HIV, while HIV incident cases remain high. To improve the effectiveness of HIV prevention and treatment service implementation, we need to understand what drives human behaviour and decision-making around HIV service use. This review highlights current thinking in the social sciences, emphasizing how understanding human behaviour can be leveraged to improve HIV service delivery. RECENT FINDINGS: The social sciences offer rich methodologies and theoretical frameworks for investigating how factors synergize to influence human behaviour and decision-making. Social-ecological models, such as the Behavioural Drivers Model (BDM), help us conceptualize and investigate the complexity of people's lives. Multistate and group-based trajectory modelling are useful tools for investigating the longitudinal nature of peoples HIV journeys. Successful HIV responses need to leverage social science approaches to design effective, efficient, and high-quality programmes. SUMMARY: To improve our HIV response, implementation scientists, interventionists, and public health officials must respond to the context in which people make decisions about their health. Translating biomedical efficacy into real-world effectiveness is not simply finding a way around contextual barriers but rather engaging with the social context in which communities use HIV services.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".