Low-frequency binge drinking: associated factors and consequences
Bibliographic record
Abstract
Background Binge drinking (BD) is a public health concern among young adults. Whether it is harmful even at a low frequency remains unclear.Objectives To test if even low-frequency (less than once a month) BD is associated with a higher likelihood of alcohol use disorder (AUD), and if some key environmental and psychological features characterize low-frequency binge drinkers.Methods Alcohol-related, environmental and psychological data were collected from university students aged 18–25 who drank alcohol. Students were divided into four groups according to their BD frequency: non-binge drinkers (never had six or more drinks on one occasion), and binge drinkers with low (at least once lifetime but less than once a month), medium (at least once a month but less than once a week), or high (at least once a week) frequency.Results Among the 3308 students included in this cross-sectional study, 473 never binged alcohol, while 1204, 1001, and 630 were low-, medium-, and high-frequency binge drinkers, respectively. Even low-frequency BD was associated with higher AUDIT scores and a larger prevalence of harmful drinking. Compared to non-binge drinkers, low-frequency binge drinkers also reported higher rates of smoking, sensation seeking, and endorsement of enhancement and social drinking motives.Conclusions Among 18–25-year-old students exposed to alcohol, even infrequent BD is associated with more harmful drinking. The findings highlight the importance of prevention strategies since BD less than once a month, which concerns a third of this population, is already associated with a higher likelihood of AUD.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".