HIV/STI prevention interventions: A systematic review and meta-analysis
Bibliographic record
Abstract
Behavioral interventions can prevent the transmission of HIV and sexually transmitted infections. This systematic review and meta-analysis assesses the effectiveness and quality of available evidence of HIV prevention interventions for people living with HIV in high-income settings. Searches were conducted in MEDLINE, EMBASE, PsycINFO, and CDC Compendium of Effective Interventions. Interventions published between January, 1998 and September, 2015 were included. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE). Forty-six articles and 63 datasets involving 14,096 individuals met inclusion criteria. Included articles were grouped by intervention type, comparison group and outcome. Few of these had high or moderate quality of evidence and statistically significant effects. One intervention type, group-level health education interventions, were effective in reducing HIV/STI incidence when compared to attention controls. A second intervention type, comprehensive risk counseling and services, was effective in reducing sexual risk behaviors when compared to both active and attention controls. All other intervention types showed no statistically significant effect or had low or very low quality of evidence. Given that the majority of interventions produced low or very low quality of evidence, researchers should commit to rigorous evaluation and high quality reporting of HIV intervention studies.
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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 | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.028 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".