Impact of identifying plausible respondents on the under-reporting of energy intake in the Canadian Community Health Survey.
Bibliographic record
Abstract
BACKGROUND: Under-reporting is common in nutrition surveys. The identification of plausible respondents is a way of measuring the impact of under-reporting on the relationship between energy intake and body mass index (BMI). DATA AND METHODS: A 24-hour dietary recall from 16,190 respondents aged 12 or older to the Canadian Community Health Survey (CCHS)--Nutrition was used to determine energy and nutrient intake. To identify plausible respondents, a confidence interval was applied to total energy expenditure derived from equations developed by the Institute of Medicine. Estimates of energy and nutrient intake for plausible respondents were compared with estimates for all respondents. Linear regression was used to demonstrate the impact of under-reporting on the relationship between reported energy intake and weight. Logistic regression was used to determine the impact of under-reporting on modelling the characteristics of obese people. RESULTS: With a confidence interval of 70% to 142% around energy expenditure, 57% of CCHS respondents were identified as "plausible respondents". Nutrient under-reporting varied between 1% and 10%. Analysis based only on plausible respondents re-establishes the theoretical relationship between energy intake and body weight, a relationship that is lost when analysis is based on the full sample. INTERPRETATION: Identifying plausible respondents is an effective way of measuring the impact of under-reporting food intake. Conclusions based on plausible respondents, rather than on all respondents, are more in line with theoretical expectations, such as a positive association between high energy intake and obesity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".