Data gaps in adolescent fertility surveillance in middle-income countries in Latin America and South Eastern Europe: Barriers to evidence-based health promotion
Bibliographic record
Abstract
Adolescent health is a major global priority. Yet, as recently described by the World Health Organization (WHO), increased recognition of the importance of adolescent health rarely transforms into action. One challenge is lack of data, particularly on adolescent fertility. Adolescent pregnancy and childbirth are widespread and affect lifetime health and social outcomes of women, men, and families. Other important components of adolescent fertility include abortion, miscarriage, and stillbirth. Access to reliable, consistently-collected data to understand the scope and complexity of adolescent fertility is critical for designing strong research, developing meaningful policies, building effective programs, and evaluating success in these domains. Vital surveillance data can be challenging to obtain in general, and particularly in low- and middle-income countries and other under-resourced settings (including rural and indigenous communities in high-income countries). Definitions also vary, making comparisons over time and across locations challenging. Informed by the Adolescence and Motherhood Research project in Brazil and considering relevance to the Southern Eastern European (SEE) context, this article focuses on challenges in surveillance data for adolescent fertility for middle-income countries. Specifically, we review the literature to: (1) discuss the importance of understanding adolescent fertility generally, and (2) highlight relevant challenges and complexity in collecting adolescent fertility data, then we (3) consider implications of data gaps on this topic for selected middle-income countries in Latin America and SEE, and (4) propose next steps to improve adolescent fertility data for evidence-based health promotion in the middle-income country 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 | 0.068 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".