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Record W2095605749 · doi:10.1136/jech.57.1.74

Comparative analysis of nutrition data from national, household, and individual levels: results from a WHO-CINDI collaborative project in Canada, Finland, Poland, and Spain

2002· article· en· W2095605749 on OpenAlexafffundabout
Lluís Serra‐Majem

Bibliographic record

VenueJournal of Epidemiology & Community Health · 2002
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsHealth CanadaDalhousie University
FundersHealth Canada
KeywordsMedicineEnvironmental healthFish <Actinopterygii>BiologyFishery

Abstract

fetched live from OpenAlex

STUDY OBJECTIVE: This project determined to what extent data on diet and nutrition, which were collected in a non-uniform manner, could be harmonised and pooled for international and national comparison. DESIGN: Direct comparisons of dietary data between studies were made using food balance sheets (FBS), household budget surveys (HBS), and individual dietary data (IDS); comparisons were also made within countries. Differences in study design and methodological approaches were taken into consideration. Data from research projects from the following four World Health Organisation (WHO) Countrywide Integrated Noncommunicable Disease Intervention (CINDI) countries were included-Canada, Finland, Poland, and Spain. MAIN RESULTS: FBS overestimated food consumption and nutrient intake compared to IDS. Results between HBS and IDS were quite similar, except for fish, meat, pulses and vegetables, which were underestimated by HBS, and sugar and honey and cereals, which were overestimated. Percentages of energy from fat, carbohydrates and proteins were higher when estimated from FBS, HBS, and IDS respectively. CONCLUSIONS: Results suggest that estimations from these three sources of dietary data are difficult to compare because they are measuring different levels of dietary information. The understanding of their relations may be important in formulating and evaluating a nutrition policy.

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)0.005
metaresearch head score (Gemma)0.004
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 score0.087
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.429
GPT teacher head0.425
Teacher spread0.003 · 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

Citations104
Published2002
Admission routes3
Has abstractyes

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