A social vaccine? Social and structural contexts of HIV vaccine acceptability among most-at-risk populations in Thailand
Bibliographic record
Abstract
A safe and efficacious preventive HIV vaccine would be a tremendous asset for low- and middle-income country (LMIC) settings, which bear the greatest global impact of AIDS. Nevertheless, substantial gaps between clinical trial efficacy and real-world effectiveness of already licensed vaccines demonstrate that availability does not guarantee uptake. In order to advance an implementation science of HIV vaccines centred on LMIC settings, we explored sociocultural and structural contexts of HIV vaccine acceptability among most-at-risk populations in Thailand, the site of the largest HIV vaccine trial ever conducted. Cross-cutting challenges for HIV vaccine uptake - social stigma, discrimination in healthcare settings and out-of-pocket vaccine cost - emerged in addition to population-specific barriers and opportunities. A 'social vaccine' describes broad sociocultural and structural interventions - culturally relevant vaccine promotion galvanised by communitarian norms, mitigating anti-gay, anti-injecting drug user and HIV-related stigma, combating discrimination in healthcare, decriminalising adult sex work and injecting drug use and providing vaccine cost subsidies - that create an enabling environment for HIV vaccine uptake among most-at-risk populations. By approaching culturally relevant social and structural interventions as integral mechanisms to the success of new HIV prevention technologies, biomedical advances may be leveraged in renewed opportunities to promote and optimise combination prevention.
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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 | 0.002 | 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 | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".