MétaCan
Menu
Back to cohort

Iron-Rich Foods Intakes among Urban Senegalese Adolescent Girls

2021· article· en· W3136592038 on OpenAlexafffundvenue
Aminata Ndéné Ndiaye, Isabelle Galibois, Sonia Blaney

Bibliographic record

VenueInternational Journal of Child Health and Nutrition · 2021
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversité de MonctonUniversité Laval
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMedicineEnvironmental healthPsychological interventionConsumption (sociology)Dietary ironFood frequency questionnairePediatricsIron deficiencyAnemiaPsychiatry

Abstract

fetched live from OpenAlex

Intake of iron-rich foods was investigated in Senegalese adolescent girls. A cross-sectional survey was conducted among 136 girls aged 13 to 18, attending two colleges in Dakar. Data on the consumption of iron-rich foods over the previous week were collected through a food frequency questionnaire. Results show that 12% of the girls had consumed dishes made with iron-rich foods 3 times or less in the past seven days, 34%, 4 to 6 times, and 54%, 7 times and above. However, 83% of the girls had anntake of iron-rich foods below the 84 g per day recommended for animal protein sources by the EAT-Lancet Commission on Healthy Diets from Sustainable Food Systems. The diet of Senegalese adolescent girls seems conducive to iron deficiency. To define effective interventions to improve the situation, investigating underlying factors to the low consumption of iron-rich foods is warranted.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.303
Teacher spread0.289 · 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

Citations3
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueInternational Journal of Child Health and NutritionSame topicChild Nutrition and Water AccessFrench-language works237,207