Validity of self-report screening for overweight and obesity: Evidence from the Canadian community health survey
Bibliographic record
Abstract
Objective: Community health surveys often collect self-report data on body height and weight for the purposes of calculating the Body Mass Index (BMI) and identifying cases of overweight and obesity. The aim of the study was to test the validity of this method and to describe age and gender trends in self-report bias in height, weight, and BMI. Methods: This population survey included 4,615 adolescents and adults from across Canada who were interviewed and then measured in their homes. Overweight and obesity were identified using self-reports and cut points in BMI. Results: Self-reports correlated highly with body measurements but on average, self-reported height was 0.88 cm greater than measured height, self-reported weight was 2.33 kg less than measured weight, and BMI derived from self-reports was 1.16 lower than BMI derived from measurements. Consequently, self-reports yielded lower rates of overweight (31.87%) and obesity (15.32%) than measurements (33.67% and 22.92%, respectively). The magnitude and variability of self-report bias in BMI were related to female gender, older age, and the presence of overweight or obesity. Discussion: Comparison of self-reported and measured height and weight indicated that most survey respondents under-reported weight and over-reported height. Intentional or not, these biases were compounded in the BMI formula and affected the accuracy of self-reports as a tool for identifying weight problems. Self-reports may be easier to collect than body measurements but should not be used exclusively as an obesity surveillance tool.
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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 | 0.040 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".