Revisiting child and adolescent health in the context of the Sustainable Development Goals
Bibliographic record
Abstract
The year 2020-five years since 189 countries signed the Sustainable Development Goals (SDGs)-has been consumed by the global response to COVID-19.To date, this pandemic has resulted in over 38 million cases and well over a million deaths [1].One collateral effect of COVID-19 has been the setting aside of many SDGs and efforts to track progress towards them.Attention to children during the pandemic has concentrated on school closures, food insecurity, and access to care within health systems taxed by COVID-19 mitigation and response efforts [2].The situation of child and adolescent health before COVID-19, and consequences of the pandemic on specific health targets for SDG 3, therefore deserve attention.As the Millennium Development Goals (MDGs) ended in 2015, activities and plans addressed the global compacts for reducing child mortality.A focus was on determinants, such as maternal and child undernutrition, gender inequities, and intersecting vulnerabilities.Because adolescents were largely ignored in the MDG process, advocacy and effort were invested to make them central to the SDG agenda.The renewed global strategy for Every Woman Every Child, launched by the UN Secretary General in 2013, was a segue to the SDGs and an effort to go beyond survival toward a transformative agenda that included healthy development [3].Advocacy for the integration of health, nutrition, and early child development led to the development of the nurturing care framework [4].As we examine the situation more than 5 years into the SDGs, several concerns emerge.Despite progress, the field remains fragmented, with limited actions in countries to develop integrated strategies for reproductive, maternal, newborn and child health (RMNCH), or inclusion of adolescent health within national plans [5].Work on the drivers of adolescent health, well being, and empowerment is underway but has yet to translate into a reasonable global strategy.This lag stems from complex, multilevel social influences during adolescence [6], insufficient disaggregation of data on adolescents, suboptimal measurement and a lack of well-defined indicators [7-9], and limited evidence on the differential impacts of social policies and programs [8] within adolescence and between adolescence and adulthood.Within health systems, many nutrition programs remain poorly integrated with other RMNCH programs and few have substantive links with sectors outside health.With the unfinished agenda for maternal, newborn and child deaths, rigorous studies to address mechanisms and hitherto unrecognized causes of excess mortality are just beginning to yield results, albeit with older pediatric age groups remaining significantly understudied, even at the simplest descriptive
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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 | 0.017 | 0.025 |
| 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 | 0.009 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.012 | 0.025 |
| Insufficient payload (model declined to judge) | 0.009 | 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".