Proceedings of the Survey Methods Section SAMPLE DESIGN OF THE 2004 CANADIAN NUTRITION SURVEY
Bibliographic record
Abstract
In order to address priority health data gaps, Statistics Canada has launched the Canadian Community Health Survey (CCHS) in 2000. As part of the CCHS biennial strategy, the provincial survey component of the second cycle of the CCHS will focus on nutrition related issues. The survey will cover persons of all ages living in private dwellings in the 10 provinces. This new survey will collect information using a 24-hour dietary recall, with repeated measures, approach in order to estimate the usual dietary intake of the Canadian population. This in turn will allow for the estimation of distribution patterns in regard to the consumption of key nutrients required for optimal health. As well, data on food insecurity and some anthropometric measurements for body weight measurement will also be collected. All this will be completed with the collection of a series of health status (chronic conditions, general health, etc.), health determinants (smoking, alcohol, physical activity, etc.) and socio-demographic characteristics. To meet the requirement of producing intake distributions for 15 key age-sex domains of interest a sample of 30,000 respondents will be selected from two sample frames. Data collection will begin in January 2004 and will extend over 12 months to reduce seasonal effects. This paper will describe several key aspects of the sample design of this new survey.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.063 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.077 | 0.017 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".