Health Behaviors in a Representative Sample of Older Canadians: Prevalences, Reported Change, Motivation to Change, and Perceived Barriers
Bibliographic record
Abstract
PURPOSE: Prevalence estimates of healthy behaviors and preventive care among older adults have not received sufficient attention, despite important health benefits such as longevity and better quality of life. Moreover, little is known about general population prevalences of older adults' efforts to change behavior, motivations to improve health behaviors, and perceived barriers to change. DESIGN AND METHODS: This study estimates the prevalence of a wide range of health behaviors and preventive-care activities, self-reported behavior change, and perceived barriers to change in a 1996-1997 population-based survey of 17,354 Canadian adults aged 60 and older. RESULTS: The findings indicate that a substantial proportion of older adults lead relatively inactive lives and often fall short of recommended standards for preventive health-care visits and screening tests. Moreover, nearly two thirds (63.2%) of older adults reported no efforts in the prior year to make changes to improve their health, and similar numbers (66.7%) indicated they thought no changes were needed. Differences in prevalences were found by gender, age, and education. IMPLICATIONS: Results from this study are useful for policy makers who need to prioritize public health efforts, researchers studying interventions, and health professionals developing preventive-care guidelines.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".