Starting at the Roots: Using Human-Centered Design to Develop a Sex and Pregnancy Education Curriculum for Adolescents in Kenya
Bibliographic record
Abstract
The rate of adolescent pregnancy worldwide remains unacceptably high. Sixteen million adolescent girls between the ages of 15 and 19, and 2 million under the age of 15 become pregnant each year. Ninety-five percent of these births occur in low-income countries, with four times the rate of adolescent pregnancy in the poorest regions of the world compared to high-income countries. There has been a shift globally to focus on the sexual and reproductive health needs of adolescents, including adolescent pregnancy. With increased awareness of this need has come a renewed call for evidence-based provision of adolescentfocused sexual and reproductive health (ASRH) services, as well as programs to prevent pregnancy in this age group. “Human-centered design” methodology is emerging as an innovative, feasible, and effective participatory approach to program design and implementation in health care. The standard Human Centered Design format includes 5 steps: (1) Understand people’s experiences, challenges, and priorities; (2) Use existing knowledge and new research to define and clarify the problem; (3) Prompt creative thinking to design many different solutions; (4) Build and workshop prototypes of ideas to quickly learn how they can be improved; and (5) Deliver solutions that meet the needs of the target population. Through this process, design team members identified a need to “help adolescents by removing the uncertainty that surrounds information on pregnancy and treatment choices”. To meet this need, we utilized participatory program development to build an adolescent-specific sex and pregnancy education program at Moi Teaching and Referral Hospital in Eldoret, Kenya.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".