The missing focus on women’s health in the First 1,000 days approach to nutrition
Bibliographic record
Abstract
The First 1,000 Days approach highlights the time between conception and a child's second birthday as a critical period where adequate nutrition is essential for adequate development and growth throughout the child's life and potentially onto their own offspring. Based on a review of relevant literature, this commentary explores the First 1,000 Days approach with a maternal lens. While the primary objective of the First 1,000 Days approach to nutrition is to reduce child malnutrition rates, particularly chronic undernutrition in the form of stunting, interventions are facilitated through mothers in terms of promoting healthy behaviours such as exclusive breast-feeding and attention to her nutritional status during pregnancy and lactation. Though these interventions were facilitated through women, women's health indicators are rarely tracked and measured, which we argue represents a missed opportunity to strengthen the evidence base for associations between maternal nutrition and women's health outcomes. Limited evidence on the effects of dietary interventions with pregnant and lactating mothers on women's health outcomes hinders advocacy efforts, which then contributes to lower prioritisation and less research.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".