Addressing social norms for adolescent timing and spacing of pregnancy in low and middle-income countries: Developing a global research agenda
Bibliographic record
Abstract
Background: Social norms shape adolescent sexual and reproductive health behaviours contributing to contraceptive and pregnancy outcomes. No global research agendas exist to guide research on adolescent social norms shifting in low- and middle-income countries (LMICs). We developed a social norms research agenda to improve adolescent healthy timing and spacing of pregnancy in LMICs. Methods: We adapted and applied the Child Health and Nutrition Research Initiative (CHNRI) method. A group of researchers guided the process, and consulted with diverse experts to develop a list of 21 research questions for global stakeholders to score via an online survey. Survey participants scored each research question according to four criteria (fills key gap, feasible, impactful, equitable). Research priority scores (RPS) and average expert agreement (AEA) statistics were calculated for each question and analysed overall and by stakeholder region and profession. Results: We received 185 survey responses. Participants were, on average, 44 years old, 64% were women, 70% were from LMICs and 47% were implementers. The RPS ranged from 52 to 81% (74% median) and the AEA ranged from 49 to 70% (58% median). Nearly 70% of stakeholders gave the same score to each of the top five research questions. The top five research priorities focused on effective norm-shifting interventions (NSIs) strategies, processes and indicators to NSIs, and NSI adaptation and scale-up. Conclusions: Using a collaborative and rigorous process with diverse representation from LMICs and implementers, we reached consensus on five priority research questions to guide future adolescent social norms research to improve healthy timing and spacing of pregnancy in LMICs.
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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 | 0.103 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".