Bibliographic record
Abstract
The complexity and importance of underage drinking prompted ERAB and ABMRF to initiate a state of the art review. It explores the extent of underage drinking across Europe and North America, as well as our current understanding of factors that increase the risk of this behaviour and potentially effective evidence-based approaches to prevent underage drinking. Unfortunately, the problem is complex and a single solution or policy to prevent underage drinking does not exist. Nevertheless, a number of strategies are effective in some circumstances and warrant further study in different populations. Preventing risky drinking requires understanding of the important influence of family and peers. It is also important to recognize that some genetic traits like impulsivity, anxiety, sensation seeking and emotional dysregulation can also influence harmful drinking. These aspects (family and peers and genetic influence) are affected by cultural and environmental influences which, in turn, can influence each other. The overall goal of this project was to develop a set of recommendations that could be used by public health departments and key stakeholders in the individual countries that make up Europe and the United States and Canada. It is clear that a single solution to this problem cannot be identified, given the different cultural backgrounds. In addition to providing a menu of effective strategies, recommendations on the best method for applying them in different cultural settings are included. Although individual interventions may have low efficacy when used in isolation, combining several interventions may improve overall effectiveness.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".