Personality differences between users of wine, beer and spirits in a community sample: the Winnipeg Health and Drinking Survey.
Bibliographic record
Abstract
OBJECTIVE: To date there are many studies describing the protective and risk factors associated with alcohol consumption and cardiovascular health (the U- or J-shaped curve). These studies have only accounted for part of the effects. One hypothesis is that personality differences may account for some of the unexplained variance. It is also unclear if wine, beer and distilled spirits have equivalent effects on health. The purpose of this study is to describe the differences in personality among users of wine, beer and spirits. METHOD: Data were from a community sample of 1,257 men and women in Winnipeg, Manitoba, Canada, that was first enrolled in 1989-90. We examined and compared the demographic and personality characteristics of wine, beer and spirits drinkers in this sample. RESULTS: The groups differed significantly on the dimensions of extraversion, psychoticism and reducer-augmenter in univariate tests. In multivariate models, for the total sample and for females, predominant drinking of wine was associated with low scores on the Vando scale (augmenters). Higher consumption of beer among males was associated with higher levels of neuroticism. CONCLUSIONS: In these instances, personality does contribute to the characterization of groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".