Policy implications of the widespread practice of ‘pre‐drinking’ or ‘pre‐gaming’ before going to public drinking establishments—are current prevention strategies backfiring?
Bibliographic record
Abstract
AIM: To describe the research, policy and prevention implications of pre-drinking or pre-gaming; that is, planned heavy drinking prior to going to a public drinking establishment. METHODS: The authors describe the phenomenon of pre-drinking, motivations for pre-drinking and its associated risks using available research literature, media and popular internet vehicles. RESULTS: Heavy drinking prior to going out has emerged as a common and celebrated practice among young adults around the world. Apparent motivations are: (i) to avoid paying for high priced drinks at commercial drinking establishments; (ii) to achieve drunkenness and enhance and extend the night out; and (iii) to socialize with friends, reduce social anxiety or enhance male group bonding before going out. Limited existing research on pre-drinking suggests that it is associated with heavy drinking and harmful consequences. We argue that policies focused upon reducing drinking in licensed premises may have the unintended consequence of displacing drinking to pre-drinking environments, possibly resulting in greater harms. CONCLUSIONS: Effective policy and prevention for drinking in licensed premises requires a comprehensive approach that takes into account the entire drinking occasion (not just drinking that occurs in the licensed environment), as well as the 'determined drunkenness' goal of some young people.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".