MétaCan
Menu
Back to cohort
Record W2619168559 · doi:10.1093/pch/pxx067

The breastfeeding paradox: Relevance for household food insecurity

2017· article· en· W2619168559 on OpenAlexaff
Isvarya Venu, Meta van den Heuvel, Jonathan P. Wong, Cornelia M. Borkhoff, Rosemary Moodie, Elizabeth Ford-Jones, Peter Wong

Bibliographic record

VenuePaediatrics & Child Health · 2017
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsBreastfeedingPovertyWorryFood securityEnvironmental healthPublic healthFood insecurityRelevance (law)BusinessEconomic growthMedicinePsychologyNursingPolitical scienceAgricultureEconomicsGeographyPediatrics

Abstract

fetched live from OpenAlex

Mitigating the harmful effects of adverse social conditions is critical to promoting optimal health and development throughout the life course. Many Canadians worry over food access or struggle with household food insecurity. Public policy positions breastfeeding as a step toward eradicating poverty. Breastfeeding fulfills food security criteria by providing the infant access to sufficient, safe and nutritious food that meets dietary needs and food preferences. Unfortunately, a breastfeeding paradox exists where infants of low-income families who would most gain from the health benefits, are least likely to breastfeed. Solving household food insecurity and breastfeeding rates may be best realized at the public policy level. Notably, the health care provider's competencies as medical expert, professional, communicator and advocate are paramount. Our commentary aims to highlight the critical link between breastfeeding and household food insecurity that may provide opportunities to affect clinical practice, public policy and child health outcomes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.054
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.016
Scholarly communication0.0040.005
Open science0.0030.004
Research integrity0.0180.017
Insufficient payload (model declined to judge)0.0030.000

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 head0.164
GPT teacher head0.417
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venuePaediatrics & Child HealthSame topicFood Security and Health in Diverse PopulationsFrench-language works237,207