Comparing subjective intoxication with risky single-occasion drinking in a European sample
Bibliographic record
Abstract
In most epidemiological literature, harmful drinking-a drinking pattern recognized as closely linked to alcohol-attributable diseases-is recorded using the measure risky single-occasion drinking (RSOD), which is based on drinking above a certain quantity. In contrast, subjective intoxication (SI) as an alternative measure can provide additional information, including the drinker's subjective perceptions and cultural influences on alcohol consumption. However, there is a lack of research comparing both. The current article investigates this comparison, using data from the Standardized European Alcohol Survey from 2015. We analysed the data of 12,512 women and 12,516 men from 17 European countries and one region. We calculated survey-weighted prevalence of SI and RSOD and compared them using Spearman rank correlation and regression models. We examined the role of the required quantity of alcohol needed for the drinker to perceive impairments and analysed additional demographic and sociodemographic characteristics as well as drinking patterns. In the most locations, the prevalence of SI was lower or equal to the prevalence of RSOD. Both prevalence estimates were highly correlated. Almost 8% of the variance in the difference between the individual-level frequencies of the SI and RSOD measures was explained by the individual quantity of alcohol needed to perceive impairments. Sociodemographic characteristics and drinking patterns explained less than 20% in the adjusted perceived quantity of alcohol needed. In conclusion, our results indicated that subjective measures of intoxication are not a preferable indicator of harmful drinking to the more conventional measures of RSOD.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".