The impact of programs for high‐risk drinkers on population levels of alcohol problems
Bibliographic record
Abstract
AIMS: Historically, treatment programs and related activities for alcoholics or high-risk drinkers have been viewed as not relevant to efforts to prevent alcohol problems, and in particular population-based prevention efforts. In this review we consider evidence that high-risk programs may have an impact on population or aggregate levels of these problems. DESIGN: We first summarize recent reviews of the clinical impact of programs for high-risk drinkers, since some level of effectiveness at the individual level is necessary for these programs to have an aggregate level impact. Following that, correlational evidence on the impact of high-risk programs on aggregate problem levels is examined. Estimates of the potential impact of high-risk programs on aggregate problem levels, based on available information on the impact of these programs and the numbers of individuals affected, are then considered, as are estimations of the comparative aggregate level impact of high-risk and consumption reduction strategies. FINDINGS: There is increasing evidence that high-risk programs have beneficial effects for individuals. Available correlational evidence supports the proposal that increases in treatment and AA have contributed to the declines in alcohol-related morbidity and mortality observed in some countries in recent years. Studies estimating the recent impact of increases in levels of treatment and AA membership support that interpretation, and studies comparing estimated effects of high-risk and population strategies find similar potential for aggregate effects. CONCLUSIONS: Programs for high-risk drinkers can have beneficial aggregate-level effects and are thus a valuable component of population-based efforts to reduce alcohol problems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".