A Cross-Sectional Study of Self-Rated Health among Older Adults: Association with Drinking Profiles and Other Determinants of Health
Bibliographic record
Abstract
This study compares the relationship between drinking profiles and self-rated health with and without adjusting for other determinants of health among a sample of older adults from the general population. Respondents were 1,494 men and 2,176 women aged between 55 and 74 from the GENACIS Canadian survey. The dependent variable was self-rated health, an individual's perception of his or her own general health, a measure used as a proxy for health status. The independent variables were drinking profiles (types of drinkers and nondrinkers) as well as other demographic, psychosocial, and health-related variables (control variables). After adjustment for other determinants of health, regression analyses showed that (1) frequent/moderate drinkers were more likely to have a better self-rated health compared with nondrinkers (lifetime abstainers and former drinkers) and (2) self-rated health did not differ significantly between frequent/moderate drinkers and other types of drinkers (frequent/nonmoderate and infrequent drinkers). Our results suggest that drinking is related to a better self-rated health compared with nondrinking regardless of the drinking profile. Drinking and healthy lifestyle guidelines specific to older adults should be studied, discussed, and integrated into public health practices.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".