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Record W2999615619 · doi:10.1093/pch/pxz172

Food insecurity in the paediatric office

2020· article· en· W2999615619 on OpenAlexaffabout
Spandana Amarthaluru, Catherine S. Birken, Meta van den Heuvel

Bibliographic record

VenuePaediatrics & Child Health · 2020
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsSickKids FoundationUniversity of Toronto
Fundersnot available
KeywordsFood insecurityPovertyIndigenousImmigrationMedicineAnxietyEnvironmental healthSingle parentFood securityPsychologyPsychiatryPolitical scienceGeographyDevelopmental psychology

Abstract

fetched live from OpenAlex

Food insecurity (FI), is defined as unreliable access to sufficient quantities of affordable and nutritious food (1,2). It affects one in six Canadian children under 18 years and is most prevalent in vulnerable groups such as those living in poverty, families headed by single mothers, Indigenous, rural, immigrant, and refugee populations (1,2). FI is associated with long-term negative outcomes including anxiety, depression, developmental concerns, and decreased school engagement (2). Given the high prevalence of FI, Canadian paediatricians will likely encounter many children exposed to FI. The American Academy of Pediatrics recommends that paediatricians address FI at each well-child visit. However, the Canadian Paediatric Society has not yet released any similar guidelines (2). To better identify families at risk of FI, a validated and brief two-item screen has been developed by Hager et al:

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)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0400.005

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 head0.123
GPT teacher head0.391
Teacher spread0.269 · 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 designNot applicable
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

Citations0
Published2020
Admission routes2
Has abstractyes

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