WORLDWIDE ALCOHOL-RELATED RESEARCH AND THE DISEASE BURDEN
Bibliographic record
Abstract
AIMS: The purpose of this study was to determine the international commitment to alcohol-related research relative to its global burden of disease, which is 4% of disability adjusted life years (DALYs). METHODS: The worldwide literature indexed in the Science Citation Index and the Social Sciences Citation Index during 1992-2003 was analysed using advanced bibliometric techniques. RESULTS: Biomedical research and the global disease burden due to alcohol both increased during 1992-2003, whilst the number of papers from alcohol-related research remained static and declined to <0.7% of all biomedical research literature. Nearly 58% of all alcohol-related research papers were from Canada and the United States, 30% from Western Europe, and 10% from Australia, New Zealand, or Japan. However, these regions suffer only 13% of the global burden of disease due to alcohol; meanwhile, the rest of the world contributed only 8% of the total research whilst suffering from 87% of the disease burden. The estimated annual expenditure on alcohol-related research in 2001 was 730 million dollars, or about 12 dollars per DALY due to alcohol. CONCLUSIONS: The global commitment to alcohol-related research is only one-sixth of that warranted by the burden of disease due to alcohol. Most such research is conducted in the developed world but is still less than that appropriate to the regional burden of disease. There is a need for more interest in alcohol-related research in the developing world, particularly in Latin America and Eastern Europe in view of their high burden of disease due to alcohol.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".