Global Mapping and Visualization of Substance Use Disorders and Treatment: Implications for Priority Setting
Bibliographic record
Abstract
Background: Understanding the global landscape of substance use disorders (SUD) and treatment is crucial for guiding future work. This bibliometric study provides a comprehensive analysis of research published between 1902 and 2025. Materials and Methods: We systematically searched the Web of Science Core Collection (SCI-Expanded) for relevant publications within the specified timeframe. Bibliometric network analyses and quantitative analysis (keyword/country co-occurrence, trends, impact, contribution) utilized R. Data included publications, topics, institutions, countries, journals, and keywords. Results: century, the growth trend accelerated, reaching 1039 publications in 2017. Key research themes identified included "Cigarette-Smoking," "Smoker," "Alcohol," "Smoking-Cessation," and "Behavioral therapy." The United States, the United Kingdom, Canada, Australia, and China were the most productive countries. The University of California, Harvard University, University of Toronto, U.S. Department of Veterans Affairs, and Veterans Health Administration (VHA) have recorded the highest scientific outputs. Alcoholism: Clinical and Experimental Research, Journal of Substance Abuse Treatment, Drug and Alcohol Dependence, Nicotine and Tobacco Research, and Addiction published the most articles. Conclusion: This study offers an objective overview of the global SUD and treatment research landscape (1902-2025). Our findings map publication trends, influential institutes, countries, journals, and key research themes. This provides a valuable reference for researchers, institutions, and journals to understand the current state and pinpoint areas for future investigation in this dynamic field.
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 | 0.022 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.096 | 0.139 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".