Replicating impact of a primary school HIV prevention programme: primary school action for better health, Kenya
Bibliographic record
Abstract
School-based programmes to combat the spread of HIV have been demonstrated to be effective over the short-term when delivered on a small scale. The question addressed here is whether results obtained with small-scale delivery are replicable in large-scale roll-out. Primary School Action for Better Health (PSABH), a programme to train teachers to deliver HIV-prevention education in upper primary-school grades in Kenya demonstrated positive impact when tested in Nyanza Province. This article reports pre-, 10-month post- and 22-month post-training results as PSABH was delivered in five additional regions of the country. A total of 26 461 students from 110 primary schools in urban and rural, middle- and low-income settings participated in this repeated cross-sectional study. Students ranged in age from 11 to 16 years, were predominantly Christian (10% Muslim), and the majority were from five different ethnic groups. Results demonstrated positive gains in knowledge, self-efficacy related to changes in sexual behaviours and condom use, acceptance of HIV+ students, endorsement of HIV-testing and behaviours to post-pone sexual debut or decrease sexual activity. These results are as strong as or stronger than those demonstrated in the original impact evaluation conducted in Nyanza Province. They support the roll-out of the programme across Kenyan primary schools.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".