Taking culture seriously in biomedical HIV prevention trials: a meta-synthesis of qualitative studies
Bibliographic record
Abstract
A substantial gap exists between widespread acknowledgement of the importance of incorporating cultural sensitivity in biomedical HIV prevention trials and empirical evidence to guide the operationalization of cultural sensitivity in these trials. We conducted a systematic literature search and qualitative meta-synthesis to explore how culture is conceptualized and operationalized in global biomedical HIV prevention trials. Across 29 studies, the majority (n = 17) were conducted in resource-limited settings. We identified four overarching themes: (1) semantic cultural sensitivity - challenges in communicating scientific terminology into local vernaculars; (2) instrumental cultural sensitivity - understanding historical experiences to guide tailoring of trial activities; (3) budgetary, logistical, and personnel implications of operationalizing cultural sensitivity; and (4) culture as an asset. Future investigations should address how sociocultural considerations are operationalized across the spectrum of trial preparedness, implementation, and dissemination in particular sociocultural contexts, including intervention studies and evaluations of the effectiveness of methods used to operationalize culturally sensitive practices.
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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 | 0.281 | 0.459 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".