Community Based HIV Prevention Intervention in Developing Countries: A Systematic Review
Bibliographic record
Abstract
Aim. To describe the features and examine effects of community based HIV prevention interventions implemented in developing countries on HIV-related knowledge and self-reported risk behavior. Background. The HIV epidemic has a significant impact on developing countries, increasing the prevalence of HIV among young persons. Community-based HIV prevention interventions have been designed to improve HIV-related knowledge and decrease engagement in risk behavior. Variations in the design and implementation of these interventions have been reported, which may influence their effectiveness. Design. Systematic review. Method. Data were extracted on the characteristics of the study and interventions and effects of the interventions on knowledge and self-report of risk behavior. Results. In total, 10 studies were included in the review. Overall, the results showed variability in theoretical underpinning, dose, and mode of delivery of the interventions. Multicomponent interventions that used mixed teaching methods produced beneficial effects on knowledge and self-reported risk behavior. Conclusion. Examining the characteristics of HIV-prevention interventions provides direction for researchers in developing efficient interventions to improve knowledge and reduce engagement in self-reported risk behavior and, in turn, decrease transmission of HIV.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".