Bibliographic record
Abstract
BACKGROUND: Although a growing body of research has examined the association between food prices and the availability of fast food restaurants on weight outcomes, there is limited empirical evidence on the direct effect of eating behavior on body weight. OBJECTIVE: The effect of eating behavior on obesity prevalence among Canadians is examined. METHODS: A nationally representative sample from the Canadian National Population Health Survey (2000-2008) with 29 722 observations is used. Obesity prevalence is estimated by a linear probability model using cross-sectional and panel estimation methods. Separate regressions are estimated for males and females. RESULTS: Multivariate analyses suggest that eating behavior has a statistically significant effect on obesity prevalence. In particular, individuals who reported excellent, very good, and good eating behavior have a lower risk of obesity compared with those with fair or poor eating behavior. Although cross-sectional and panel data methods produce consistent results, the cross-sectional model overestimates the effect of eating behavior on the risk of obesity. This highlights the importance of controlling for unobserved individual factors that may affect how eating behavior is related to body weight. CONCLUSION: Evidence is found showing that eating behavior is an important determinant of obesity prevalence. The findings suggest that improving the eating behavior of individuals would help reduce excessive body weight and its induced health risks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".