Assessing the impacts of alcohol policies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Alcohol policies have significant potential to curb alcohol-related harms, improve health, increase productivity, reduce crime and violence, and cut government expenditure. The WHO Global Strategy to reduce the harmful use of alcohol provides a menu of policy options based on international consensus, which the OECD has used as a starting point in identifying a set of policies to be assessed in an economic analysis based on a computer simulation approach. This working paper provides a comprehensive illustration of the modelling approach, input data and underlying assumptions that have been used to carry out the analyses. The policies assessed in three country settings – Canada, the Czech Republic and Germany – include price policies, regulation and enforcement policies, education programmes and health care interventions. The results of the OECD analyses show that brief interventions in primary care, typically targeting high-risk drinkers, and tax increases, which affect all drinkers, have the potential to generate large health gains. The impacts of regulation and enforcement policies as well as other health care interventions are more dependent on the setting and mode of implementation, while school-based programmes show less promise. Alcohol policies have the potential to prevent alcohol-related disabilities and injuries in hundreds of thousands of working-age people in the countries examined, with major potential gains in their productivity. Most alcohol policies are estimated to cut health care expenditures to the extent that their implementation costs would be more than offset. Health care interventions and enforcement of drinking-and-driving restrictions are more expensive policies, but they still have very favourable cost-effectiveness profiles.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it