Prevalence and Correlates of Overweight and Obesity Among Older Adults: Findings From the Canadian National Population Health Survey
Bibliographic record
Abstract
BACKGROUND: The prevalence of obesity among elderly persons in industrialized countries ranges from 15% to 20%. Little is known about variations of overweight within subgroups of the elderly population. This study examined the factors associated with overweight and obesity among older men and women. METHODS: Data for 12,823 community-dwelling persons aged 65 and older from the 1996-1997 Canadian National Population Health Survey were examined. Predictors of overweight (body mass index [BMI] = 25.0-29.9 kg/m2) and obesity (BMI = >30 kg/m2) relative to normal weight (BMI = 20.0-24.9 kg/m2) were examined using logistic regression analyses. Analyses were stratified by gender. The predictor variables included age, education, marital status, place of birth, region, smoking status, alcohol use, chronic conditions, physical activity, functional limitations, self-rated health, social support, and psychological distress. RESULTS: Overall, 39% and 13% of Canadian older adults were classified as overweight and obese, respectively. Some of the risk factors for overweight were male gender, low education, being married, Canadian born, residence in the Atlantic provinces, no use of alcohol, comorbidity, physical inactivity, and limited functional status. Risk factors for obesity were similar to those for overweight except for being unmarried; American, European, and Australian born; lower and higher levels of alcohol use; poor self-rated health; and psychological distress. CONCLUSIONS: The results could lead to more effective weight-control interventions that are designed to promote increased physical activity and healthy eating habits among obese older individuals.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".