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Record W2889884168 · doi:10.3148/66.2.2005.67

<i>Development and Validation</i>of a Food Frequency Questionnaire

2005· article· en· W2889884168 on OpenAlexaffvenueabout
Bryna Shatenstein, Sylvie Nadon, Catherine Godin, Guylaine Ferland

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2005
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsUniversité de MontréalInstitut Universitaire de Gériatrie de Montréal
Fundersnot available
KeywordsQuartileFood frequency questionnaireMedicineDemographyPopulationGerontologyEnvironmental healthConfidence intervalInternal medicine

Abstract

fetched live from OpenAlex

Regular diet monitoring requires a tool validated in the target population. A 73-item, semiquantitative, self-administered food frequency questionnaire (FFQ), was adapted in French and English from the Block National Cancer Institute Health Habits and History Questionnaire. The FFQ was used to capture usual long-term food consumption among adults living in Quebec. A representative sample of adults aged 18 to 82 (57% female) was recruited by random digit dialling in the Montreal region. Approximately 64% of recruits completed and returned the instrument by mail (n=248). The FFQ was validated in a subsample (n=94, 61% female) using four nonconsecutive food records (FRs). Median energy intakes (in kcal) for men and women, respectively, were FFQ (total sample) 2,112 and 1,823, FFQ (subsample) 2,137 and 1,752, and FR (subsample) 2,510 and 1,830. Spearman correlation analyses between FFQ and FR nutrients were positive (with r ranging from 0.32 for folate to 0.58 for saturated fatty acids) and statistically significant (p<0.001), with better results among women. On average, cross-classification of energy and 24 nutrients from the FFQ and means of four FRs placed 39% into identical quartiles and 78% into identical and contiguous quartiles, with only 4% frankly misclassified. These results suggest that the FFQ is a relatively valid instrument for determining usual diet in Quebec adults.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.069
GPT teacher head0.367
Teacher spread0.298 · 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 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

Citations95
Published2005
Admission routes3
Has abstractyes

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