Older Adults' Contact With Health Practitioners: Is There an Association With Smoking Practices?
Bibliographic record
Abstract
BACKGROUND: Approximately 12% of the North American population aged 65 and older smoke cigarettes daily. Late-life smokers represent an important population for intervention by health practitioners. The objective of this study was to determine the extent to which contact with health practitioners (dentists or physicians) affects smoking status among older adults. METHODS: We used data on a probability-based sample of community-dwelling elderly respondents (N = 13,363) from the Canadian 1996-1997 National Population Health Survey. Descriptive statistics were calculated, and multivariate logistic regression analysis was performed to examine the associations between current tobacco use and contact with health care practitioners controlling for potential confounders, especially sociodemographic characteristics, selected health conditions, self-reported health, body mass index, functional status, perceived social support, and psychological distress. RESULTS: Older adults without a regular physician (adjusted odds ratio [AOR], 1.33; 95% confidence interval [CI], 1.11-1.59), with infrequent physical (AOR, 1.22; 95% CI, 1.07-1.40), and dental (AOR, 2.68; 95% CI, 2.07-3.47) checkups were more likely to be current smokers. Age (younger), church attendance (infrequent), drinking behavior (former or occasional), body mass index (normal weight), and psychological distress were all independently related to current smoking. CONCLUSIONS: Results indicate that patients' contact with health care providers is strongly negatively associated with smoking. More specific data are needed to learn the frequency with which physicians and dental professionals attempt to modify older individuals' smoking behavior and the degree to which such efforts are effective.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".