Markers of Sedentarism: The Joint Canada/U.S. Survey of Health
Bibliographic record
Abstract
BACKGROUND: The Joint Canada/United States Survey of Health (JCUSH) was a one-time collaborative survey undertaken by Statistics Canada and the National Center for Health Statistics. METHODS: This analysis provides country-, sex-, and age-specific comparative markers of adult obesity and sedentarism, defined as independent and collective groupings of self-reported leisure-time inactivity (<1.5 MET-hours/day), usual occupational sitting, and no/low active transportation (<1 hour/week). Logistic regression assessed the likelihood of sedentarism in U.S. vs. Canada, with and without adjusting for BMI-defined obesity categories: healthy weight (18.5 ≤ BMI <25 kg/m2; n = 3542), overweight (25 ≤ BMI < 30 kg/m2; n = 2,651), and obesity (BMI ≥ 30 kg/m2; n = 1470). RESULTS: Compared with Canadians, U.S. adults are 24% more likely to be overweight/ obese, 59% more likely to be inactive in leisure-time, 19% more likely to report no/low active transportation, and 39% more likely to collectively report all sedentarism markers, adjusting for sex and age. Focusing on obese individuals in both countries, obese U.S. residents were 90% more likely to be inactive during leisure-time, 41% more likely to report no/low active transportation, and 73% more likely to report all sedentarism markers. CONCLUSIONS: This ecological analysis sheds light on differential risks of obesity and sedentarism in these neighboring countries.
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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 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".