Bridging the know-do gap: improved adolescent sexual and reproductive health and health information systems in West Africa and the Middle East
Bibliographic record
Abstract
BACKGROUND: As the world approaches the final five years to achieve the Sustainable Development Goals (SDGs), a global polycrisis threatens progress on health and well-being. Populations experiencing vulnerabilities, particularly women, girls, and adolescents in low- and middle-income countries, shoulder a disproportionate burden. Universal access to sexual and reproductive health (SRH) services has seen mixed progress. In particular, adolescents are facing heightened risks such as early marriage, unintended pregnancies, and limited access to SRH services, compounded by gender norms and fragile contexts. Integrated, community-driven, and gender-transformative approaches are urgently needed, as are robust health data and information systems. However, fragmented systems suffer from poor data quality and underrepresentation of marginalized groups. Digital innovations hold promise for addressing these gaps but must be adapted to local contexts. BRIDGING THE KNOW-DO GAP: This supplement presents findings from the Cedar Cohort, an initiative supported by the International Development Research Centre across West Africa and the Middle East (2017-2023). The cohort's 16 implementation research projects explored SRH services for adolescents and health information systems, emphasizing gender equality, human rights, and knowledge translation. The articles are organized around three themes: (1) adopting contextual approaches rooted in local communities and their context-specific needs, (2) advancing gender equality and inclusion and (3) harnessing the transformational potential of data and information systems. Authored by LMIC-based researchers, the supplement provides concrete findings and localized insights into the transformative change possible when high-quality research is combined with an understanding of social, economic, political, and cultural factors at the individual, organizational, structural and systems levels. CONCLUSION: The collection of research projects featured in this supplement enriches our understanding of designing and implementing effective initiatives to improve health outcomes for women, children and adolescents. The findings emphasize that no singular solution exists for the multifaceted SRH challenges that continue to evolve. The projects in the Cedar Cohort offer critical insights into designing and scaling SRH interventions that are locally relevant, inclusive, and impactful. The current polycrisis necessitates a re-evaluation of global strategies, and the findings from the research projects in this supplement provide indications of ways forward.
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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 | 0.005 | 0.000 |
| 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 | 0.000 |
| 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".