The Bias in Self‐reported Obesity From 1976 to 2005: A Canada–US Comparison
Bibliographic record
Abstract
The objective of this study was to determine whether the bias in self-reported estimates of obesity has changed over time and followed different patterns in Canada and the United States. Using age-standardized data from three waves of the National Health and Nutrition Examination Survey (NHANES) in the United States and the Canadian Community Health Survey (CCHS) and the Canadian Heart Health Survey (CHHS) in Canada, discrepancies were compared between reported and measured estimates of height, weight, and obesity (based on the BMI) from 1976 to 2005. Results indicated that obesity increased in both countries, but rates were higher in the United States. The discrepancy between self-reported and measured obesity was small in the United States with reported data underestimating measured prevalence by about 3%; this stayed relatively constant over time. In Canada, the discrepancy was large and doubled in the past decade (from 4 to 8%). In the United States, self-reported data may be more accurate in monitoring changes in obesity over time, as the estimates have consistently remained about 3% below the measured estimates, whereas in Canada, monitoring obesity based solely on self-reported height and weight may produce inaccurate estimates because of the increasing discrepancy between self-reported and measured data.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".