Piloting an evidence-based intervention for HIV prevention among street youth in Eldoret, Kenya
Bibliographic record
Abstract
OBJECTIVES: This study presents findings from piloting an adapted evidence-based intervention, Stepping Stones and Creating Futures, to change street-connected young people's HIV knowledge, condom-use self-efficacy, and sexual practices. METHODS: Eighty street-connected young people participated in a pre- and post-test mixed methods design in Eldoret, Kenya. The primary outcome of interest was HIV knowledge. Secondary outcomes included condom-use self-efficacy and sexual practices. Multiple linear regression models for change scores with adjustment for socio-demographic variables were fitted. Qualitative and quantitative findings are presented together, where integration confirms, expands on, or uncovers discordant findings. RESULTS: Participants had a significant increase in HIV knowledge from pre- to post-intervention. The median HIV knowledge score pre-intervention was 11 (IQR 8-13) and post-intervention 14 (IQR 12-16). Attendance was significantly associated with HIV knowledge change scores. Qualitatively participants reported increased HIV and condom-use knowledge and improved condom-use self-efficacy and health-seeking practices. CONCLUSIONS: Our findings support the potential for further testing with a rigorous study design to investigate how best to tailor the intervention, particularly by gender, and increase the overall effectiveness of the program.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".