Reasons for drinking wine and other beverages – comparison across motives in older adults
Bibliographic record
Abstract
Carmen C Moran, Anthony J SalibaSchool of Psychology, Charles Sturt University, Wagga Wagga, NSW, AustraliaObjectives: Health as a positive reason for drinking wine (eg, antioxidant content) has scant empirical data to inform policy. This study attempted to examine that motive by including health as one of six motives for drinking, along with measures of problem drinking (the Cut-down, Annoyed, Guilty, Eye-opener [CAGE] questionnaire) in an older adult population.Design: Four drinking motives (enhancement, coping, social, and conformity), plus taste and health were included within a larger national telephone survey on drinking behaviors. We also recorded beverage preference.Results: In this analysis, 705 participants drank a preferred beverage. Taste was the most highly endorsed motive. Just under one quarter of the sample endorsed health as a positive reason for drinking. After controlling for age, sex, and preferred alcoholic beverage, the internal psychological motives of enhancement and coping predicted CAGE scores, but external motives did not. Believing that alcohol is healthy was a negative predictor of CAGE scores. Our results showed a different pattern to those with younger drinkers reported in previous research. Our older group was less likely to drink for social reasons and internal motives were predictive of CAGE scores.Conclusion: A motives-based approach to managing problem drinking will need to take account of a wider range of age-related motives. Based on the current data, there is little reason to suspect drinking wine for health reasons is associated with potential problem drinking.Keywords: drinking behavior, wine and drinking motives, healthy drinking, wine and health
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".