High Intensity Drinking (HID) Assessed by Maximum Quantity Consumed Is an Important Pattern Measure Adding Predictive Value in Higher and Lower Income Societies for Modeling Alcohol-Related Problems
Bibliographic record
Abstract
Adjusting for demographics and standard drinking measures, High Intensity Drinking (HID), indexed by the maximum quantity consumed in a single day in the past 12 months, may be valuable in predicting alcohol dependence other harms across high and low income societies. The data consisted of 17 surveys of adult (15,460 current drinkers; 71% of total surveyed) in Europe (3), the Americas (8), Africa (2), and Asia/Australia (4). Gender-disaggregated country analyses used Poison regression to investigate whether HID (8-11, 12-23, 24+ drinks) was incrementally influential, beyond log drinking volume and HED (Heavy Episodic Drinking, or 5+ days), in predicting drinking problems, adjusting for age and marital status. In adjusted models predicting AUDIT-5 for men, adding HID improved the overall model fit for 11 of 15 countries. For women, 12 of 14 countries with available data showed an improved fit with HID included. The results for the five Life-Area Harms were similar for men. Considering the results by gender, each country showing improvements in model fit by adding HID had larger values of the average difference between high intensity and usual consumption, implying variations in amounts consumed on any given day. The amount consumed/day often greatly exceeded HED levels. In many societies of varying income levels, as hypothesized, HID provided important added information on drinking patterns for predicting harms, beyond the standard volume and binging indicators.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".