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Record W87295651 · doi:10.24095/hpcdp.32.2.03

Dietary supplement use and iron, zinc and folate intake in pregnant women in London, Ontario

2012· article· en· W87295651 on OpenAlexafffundvenueabout
A.B. Roy, Susan Evers, M. Karen Campbell

Bibliographic record

VenueChronic diseases and injuries in Canada · 2012
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsChildren’s Health Research InstituteLawson Health Research InstituteUniversity of GuelphUniversity of CalgaryWestern University
FundersCanadian Institutes of Health Research
KeywordsMultivitaminMicronutrientMedicineDietary Reference IntakePregnancyEnvironmental healthReference Daily IntakeDietary supplementFood frequency questionnaireFolic acidFood groupNutrientVitaminFood scienceBiologyEndocrinologyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: We examined the dietary intake of iron, zinc and folate, estimated from both food and supplement sources, in 2019 pregnant women who participated in the Prenatal Health Project (PHP). The PHP recruited pregnant women from ultrasound clinics in London, Ontario, in the years 2002-2005. METHODS: Participants completed a telephone survey, which included a food frequency questionnaire and questions on dietary supplement use. Frequencies of use of dietary supplements were generated. Nutrient intake values were estimated from food and supplement sources, and summed to calculate total daily intake values. RESULTS: Most women took a multivitamin supplement, and many women took folic acid and iron supplements; however, one-fifth of the sample did not take any supplements providing any of the three micronutrients. Despite being of a higher socio-economic status overall, significant proportions of the cohort ranked below the recommended dietary allowance values for iron, zinc, and folate. This suggests there may be other barriers that impact dietary practices. CONCLUSIONS: Further research is required on how to better promote supplement use and a healthy diet during pregnancy.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.028
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.251
Teacher spread0.238 · 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.

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

Citations24
Published2012
Admission routes4
Has abstractyes

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