Nurturing children's development through healthy eating and active living: Time for policies to support effective interventions in the context of responsive emotional support and early learning
Bibliographic record
Abstract
Fostering the growth, development, health, and wellbeing of children is a global priority. The early childhood period presents a critical window to influence lifelong trajectories, however urgent multisectoral action is needed to ensure that families are adequately supported to nurture their children's growth and development. With a shared vision to give every child the best start in life, thus helping them reach their full developmental potential, we have formed the International Healthy Eating Active Living Matters (HEALing Matters) Alliance. Together, we form a global network of academics and practitioners working across child health and development, and who are dedicated to improving health equity for children and their families. Our goal is to ensure that all families are free from structural inequality and oppression and are empowered to nurture their children's growth and development through healthy eating and physical activity within the context of responsive emotional support, safety and security, and opportunities for early learning. To date, there have been disparate approaches to promoting these objectives across the health, community service, and education sectors. The crucial importance of our collective work is to bring these priorities for early childhood together through multisectoral interventions, and in so doing tackle head on siloed approaches. In this Policy paper, we draw upon extensive research and call for collective action to promote equity and foster positive developmental trajectories for all children. We call for the delivery of evidence-based programs, policies, and services that are co-designed to meet the needs of all children and families and address structural and systemic inequalities. Moving beyond the "what" is needed to foster the best start to life for all children, we provide recommendations of "how" we can do this. Such collective impact will facilitate intergenerational progression that builds human capital in future generations.
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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 | 0.028 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.005 | 0.028 |
| Research integrity | 0.015 | 0.027 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".