Breaking barriers in the prevention of adolescent pregnancies for in-school children in Kirehe district (Rwanda): a mixed-method study for the development of a peer education program on sexual and reproductive health
Bibliographic record
Abstract
BACKGROUND: Despite a variety of mainly school-driven prevention strategies, the number of adolescent pregnancies in Rwanda is worryingly high and is even expected to increase. The aim of this study is to empower Kirehe secondary school students aged 15-19 years old in sexual and reproductive health (SRH) by developing a peer education program. METHODS: A combination of quantitative and qualitative research will be used. A pre- and post-survey will examine adolescents' knowledge and attitudes regarding SRH. In addition, six focus group interviews will explore these knowledge, attitudes but also SRH needs more in depth. Based on the obtained information, and after retrieving experts' input, a peer education program is being developed in which Midwifery students obtain training in SRH and educational skills (= first train-the-trainer module). In turn, these students will educate and train a selected group of secondary school students (= second train the trainer module). Finally, these trained in-school students can act as reliable peers for other in-school students in the context of SRH. DISCUSSION: The project will contribute to 1) more independent and thoughtful decisions in contraception and sexual behavior, and consequently less adolescent pregnancies, and 2) the reinforcement of the Rwandan Midwifery education. TRIAL REGISTRATION: University of Rwanda, College of Medicine and Health Sciences, Institutional Review Board, Approval No 158/CMHS IRB/2019.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".