A CROSS-NATIONAL COMPARISON OF THE EFFECT OF SMOKING AND DRINKING BEHAVIOR ON COGNITIVE HEALTH IN TAIWAN AND THE US
Bibliographic record
Abstract
Abstract Previous studies have shown that smoking tobacco and alcohol consumption impact individuals’ cognitive health. However, less studies have focused on cross-national comparisons of the effects of smoking and alcohol consumption on cognitive health and their differential effects across gender. This cross-sectional study used the Health and Retirement Study (HRS) 2018 (N= 7,467) and the Taiwan Longitudinal Study in Aging (TLSA) 2019 (N=2,776) for comparison. The cognitive function scores of the HRS are from the Telephone Interview of Cognitive Status-Modified (TICS-M; range: 0-35). For the TLSA, the Montreal Cognitive Assessment (MoCA; range: 0-30) is used. Our results show that both smoking and drinking negatively affect one’s cognitive health among the US and Taiwanese populations, yet important gender differences were found. Taiwanese women aged 60 and older were found to have lower cognitive scores relative to otherwise similar men; however, in the United States, middle-aged and older women were found to perform better in their cognitive tests than their male counterparts. No moderating effects of gender on smoking were found among older persons from both countries. Some drinking was found to be protective for Taiwanese women, while binge drinking among US women further negatively impacted their cognitive health. Findings suggest that both smoking and drinking may impose negative influence on one’s cognitive health and these impacts differ by gender. These differences may be the result of differences in the social context and cultural meaning of health behaviors. Further research is needed to address gender disparities in health behaviors.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".