Age and Sex-Specific Associations in Health Risk Factors for Chronic Disease: Evidence from the Atlantic Partnership for Tomorrow’s Health (PATH) Cohort
Bibliographic record
Abstract
The objective of this study was to discern health risk factors for chronic disease by age and sex in a Canadian cohort. Participants of the Atlantic Partnership for Tomorrow's Health (PATH) cohort with health risk factor data (physical activity, smoking, alcohol consumption, diet, body mass index [BMI]) were included (n = 16,165). Multivariable logistic regression models were used to evaluate the relationship among health risk factors, age, and sex. Regression analysis revealed that the odds of engaging in high levels of physical activity and having a BMI ≥ 25 was lower for females than males across all age groups, whereas the odds of abdominal obesity was substantially higher for females of all ages than for males. The odds of habitually consuming alcohol was lower for females of all ages than for males, and the odds of being a former/current smoker was lower for older (57-74 years of age) females than for males. The odds of consuming five or more servings of fruit and vegetables per day was higher for females of all ages than for males. There are evident differences in health risk factors for males and for females, as well as across age groups, and public health efforts need to account for the role played by sex and age in addressing chronic disease burden in Canadian adults.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".