Smoking and alcohol consumption patterns among elderly Canadians with mobility disabilities
Bibliographic record
Abstract
BACKGROUND: Mobility disability is a major adverse health outcome associated with aging and an impediment to older adults' well-being and behaviors in social and leisure activities. It has been shown that lifestyle factors, including smoking and alcohol consumption, have been used as coping strategies to deal with the negative impact of disability. The aim of this study was to determine the prevalence of smoking and alcohol consumption among older Canadians with different levels of mobility disabilities and to examine factors associated with these two lifestyle patterns among those with disabilities. METHODS: Secondary data analysis was performed using individuals (n = 6,038) aged 65 years and older from both the 2001 Participation and Activity Limitation Survey and the 2003 Canadian Community Health Survey. Multivariate logistic regressions examined the relationship between disability severity and smoking as well as alcohol consumption while controlling for potential confounding socioeconomic factors. RESULTS: The proportion of current smokers among seniors with less-severe and more-severe mobility disabilities and those in the general population was comparable with 12.55%, 11.57% and 11.93%, respectively. Forty-eight percent of seniors in the general population consumed alcohol regularly, compared to only 12.85% with more-severe mobility disabilities. No significant association was shown between the severity level of mobility disabilities and smoking (odds ratio = 0.90, 95% confidence interval: 0.75, 1.08). However, seniors having more-severe disability were less likely to consume alcohol regularly (odds ratio = 0.76, 95% confidence interval: 0.65, 0.89). Other variables including age, gender, income, living status, and social participation also impacted these lifestyle patterns among the study population. CONCLUSIONS: Smoking and alcohol patterns present different associations with the severity level of mobility disabilities. Compared with the general population, elderly Canadians with mobility disabilities had similar smoking prevalence but differ significantly in terms of alcohol consumption. Results from this research will be relevant to decision makers involved in program planning, health education, and policy development as it pertains to the prevention and management of age-related disability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".