Balancing evidence-informed and user-responsive design: Experience with human-centered design to generate layered economic empowerment and SRH programming in Tanzania, Ethiopia, and Nigeria
Bibliographic record
Abstract
In 2021, the Adolescents 360 (A360) project pursued a human-centered design (HCD) process to layer complementary economic empowerment components on top of its existing sexual and reproductive health (SRH) interventions targeting adolescent girls aged 15 to 19. Given the volume of evidence informing successful approaches for improving economic and empowerment outcomes for adolescents, we pursued an intentionally evidence-informed and gender-intentional design process, while trying to also respond directly to user insights. In this open letter, we share how we utilized and validated the evidence-base while applying the core tenets of HCD (empathy and user insights) to design holistic, layered programming for girls. We describe three overarching categories which depict how we used the existing evidence and new user insights to strengthen our design process. Often the evidence base allowed us to expedite finding a solution that worked for our users. However, at times there was a disconnect between what we knew worked in the evidence base and what our users said they wanted. New insights also allowed us to build a greater understanding of our users' lived experiences where there were existing evidence gaps. We were aided by the engagement of a technical partner, BRAC, who synthesized evidence for our design teams and functioned as an 'on demand' support mechanism as questions and challenges arose. Yet, the volume of information to absorb almost guaranteed that we would miss out on the opportunity to apply certain evidence-based practices. We encourage researchers to consider how to make evidence more easily digestible to practitioners and for the whole community of practice to work together to identify what questions need to be asked to effectively operationalize evidence in a local context.
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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 | 0.114 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".