Quantifying alcohol’s harm to others: a research and policy proposal
Bibliographic record
Abstract
Just under 2.5 million people die annually due to alcohol use. This global estimate, however, excludes most of the health burden borne by others than the alcohol user. Alcohol's harm to others includes a multitude of conditions, such as trauma from traffic crashes, fetal disorders due to prenatal exposure to alcohol, as well as interpersonal and intimate partner violence. While alcohol's causal role in these conditions is well-established, alcohol's harm to others' contribution to the overall health burden of alcohol remains unknown. This knowledge gap leads to a situation in which alcohol policy and prevention strategies largely focus on the reduction of alcohol's detrimental health harms on the alcohol users, neglecting affected others and population groups most vulnerable to these harms, including women and children. In this article, we seek to elucidate why estimates for alcohol's harm to others are lacking and offer guidance for future research. We also argue that a full assessment of the alcohol health burden that includes the harm caused by others' alcohol use would enhance the visibility and public awareness of such harms, and advancing the evaluation of policy interventions to mitigate them.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".