Promoting sexual and reproductive health among adolescents in southern and eastern Africa (PREPARE): project design and conceptual framework
Bibliographic record
Abstract
BACKGROUND: Young people in sub-Saharan Africa are affected by the HIV pandemic to a greater extent than young people elsewhere and effective HIV-preventive intervention programmes are urgently needed. The present article presents the rationale behind an EU-funded research project (PREPARE) examining effects of community-based (school delivered) interventions conducted in four sites in sub-Saharan Africa. One intervention focuses on changing beliefs and cognitions related to sexual practices (Mankweng, Limpopo, South Africa). Another promotes improved parent-offspring communication on sexuality (Kampala, Uganda). Two further interventions are more comprehensive aiming to promote healthy sexual practices. One of these (Western Cape, South Africa) also aims to reduce intimate partner violence while the other (Dar es Salaam, Tanzania) utilises school-based peer education. METHODS/DESIGN: A modified Intervention Mapping approach is used to develop all programmes. Cluster randomised controlled trials of programmes delivered to school students aged 12-14 will be conducted in each study site. Schools will be randomly allocated (after matching or stratification) to intervention and delayed intervention arms. Baseline surveys at each site are followed by interventions and then by one (Kampala and Limpopo) or two (Western Cape and Dar es Salaam) post-intervention data collections. Questionnaires include questions common for all sites and are partly based on a set of social cognition models previously applied to the study of HIV-preventive behaviours. Data from all sites will be merged in order to compare prevalence and associations across sites on core variables. Power is set to .80 or higher and significance level to .05 or lower in order to detect intervention effects. Intraclass correlations will be estimated from previous surveys carried out at each site. DISCUSSION: We expect PREPARE interventions to have an impact on hypothesized determinants of risky sexual behaviour and in Western Cape and Dar es Salaam to change sexual practices. Results from PREPARE will (i) identify modifiable cognitions and social processes related to risky sexual behaviour and (ii) identify promising intervention approaches among young adolescents in sub-Saharan cultures and contexts. TRIAL REGISTRATIONS: Controlled Trials ISRCTN56270821 (Cape Town); Controlled Trials ISRCTN10386599 (Limpopo); Clinical Trials NCT01772628 (Kampala); Australian New Zealand Clinical Trials Registry ACTRN12613000900718 (Dar es Salaam).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".