Bias in self-reported estimates of obesity in Canadian health surveys: an update on correction equations for adults.
Bibliographic record
Abstract
BACKGROUND: This study compares the bias in self-reported height, weight and body mass index (BMI) in the 2008 and 2005 Canadian Community Health Surveys and the 2007 to 2009 Canadian Health Measures Survey. The feasibility of using correction equations to adjust self-reported 2008 Canadian Community Health Survey values to more closely approximate measured values is assessed. DATA AND METHODS: Data are from the 2008 and 2005 Canadian Community Health Surveys and the 2007 to 2009 Canadian Health Measures Survey. In these surveys, respondents reported their height and weight, and were subsequently measured. Regression equations based on the 2007 to 2009 Canadian Health Measures Survey and the 2005 Canadian Community Health Survey were applied to self-reported 2008 Canadian Community Health Survey data. These equations predicted measured BMI based on self-reported BMI. RESULTS: The bias in reporting height was similar across all three surveys, but the bias in reporting weight was larger in the two Canadian Community Health Surveys, and as a result, discrepancies in estimates of obesity between self-reported and measured values were greater. Application of correction equations based on 2005 Canadian Community Health Survey data to self-reported values in the 2008 Canadian Community Health Survey produced more accurate estimates of obesity than did equations based on Canadian Health Measures Survey data. INTERPRETATION: Survey context may influence the magnitude of the bias in self-reported weight. Respondents who are aware that they will be weighed may report their weight more accurately. Additional data points are required to determine whether the bias in self-reported measures in the Canadian Community Health Survey is changing.
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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 | 0.056 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".