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Record W3092149163 · doi:10.1017/s1368980020003894

The missing focus on women’s health in the First 1,000 days approach to nutrition

2020· review· en· W3092149163 on OpenAlexafffund
Mai‐Lei Woo Kinshella, Sophie E. Moore, Rajavel Elango

Bibliographic record

VenuePublic Health Nutrition · 2020
Typereview
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of British Columbia
FundersMedical Research CouncilCanadian Institutes of Health ResearchGovernment of Canada
KeywordsPsychological interventionMalnutritionPregnancyMedicineBreast feedingBreastfeedingLactationEnvironmental healthOffspringGerontologyDevelopmental psychologyPsychologyPediatricsNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.855
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.003
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.089
GPT teacher head0.356
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations27
Published2020
Admission routes2
Has abstractyes

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