Using Human-Centered Design to Develop, Launch, and Evaluate a National Digital Health Platform to Improve Reproductive Health for Rwandan Youth
Bibliographic record
Abstract
BACKGROUND: A lack of access to evidence-based, unbiased, and youth-friendly family planning and reproductive health (FP/RH) information and care limit young people's ability to prevent unplanned pregnancies and HIV and sexually transmitted infections. This threat-ens their health and is a significant cause of school drop-out, limiting young peoples' well-being, future potential, and employment opportunities. To address these challenges facing youth, YLabs used an end-to-end human-centered design (HCD) approach to create CyberRwanda, a digital platform aiming to improve the health and livelihoods of adolescents (aged 12-19 years) in Rwanda. DESIGNING FOR DIGITAL WITH YOUTH: From 2016 to 2020, CyberRwanda was designed and piloted using an HCD approach in partnership with more than 1,000 youth, parents, teachers, and public and private health care providers. During the problem recognition phase, HCD revealed participants' beliefs, behavioral preferences, and experiences as they relate to FP/RH specifically and their broader life experiences, motivations, and challenges. Several phases of analog, digital, and live prototyping with youth and key stakeholders were used to codesign, test, and refine the intervention for implementation. RESULTS: CyberRwanda is a direct-to-consumer platform where adolescents can learn integrated, age-appropriate health, and skills-building information through edutainment behavior change stories and a robust frequently asked questions library, order health products online, and be linked to CyberRwanda's network of private and public health care providers who have been trained to provide adolescent-friendly care. IMPLICATIONS FOR FUTURE RESEARCH: The HCD process resulted in significant pivots to the design of the digital platform and the implementation model. Using HCD provided a structured methodology to combine technical FP/RH expertise and visual and product design expertise to codesign and iteratively develop a digital health intervention with and for Rwandan youth.
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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 | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".