Implementing and Sustaining Effective Alcohol‐Related Policies at the Local Level: Evidence, Challenges, and Next Steps
Bibliographic record
Abstract
Alcohol‐related problems are experienced most directly at the local level. There have been significant strides in evaluating locally based alcohol policies and prevention strategies. This article draws from this existing body of research to answer three questions: What is known about effective local interventions and policies? What are the main challenges facing local action on alcohol and how can those challenges be addressed? How can local action on alcohol be sustained? This article reviews evaluated local alcohol interventions and policies, focusing on several countries where these initiatives have been evaluated: for example, Australia, Canada, Finland, New Zealand, Norway, Sweden, and the United States. The positive outcomes associated with community‐based initiatives are summarized and features of successful local action are identified. Although current research finds a number of positive outcomes of local alcohol interventions and policies, a number of challenges of this work remain; these challenges include providing adequate training, resources, and tools for local action; building local resource streams and coalitions to sustain expertise; sustaining long‐term commitment to monitor and evaluate the effects of policies; and addressing the vested interests of community stakeholders in alcohol policy efforts. Lessons learned and recommendations for future community‐based alcohol prevention initiatives are drawn from the findings and challenges of current work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.065 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".