Global research mapping of substance use disorder and treatment 1971–2017: implications for priority setting
Bibliographic record
Abstract
BACKGROUND: Globally, substance use disorders are prevalent and remain an intractable public health problem for health care systems. This study aims to provide a global picture of substance use disorders research. METHODS: The Web of Science platform was used to perform a cross-sectional analysis of scientific articles on substance use disorders and treatment. Characteristics of publication volume, impact, growth, authors, institutions, countries, and journals were examined using descriptive analysis and network visualization graphs. RESULTS: Thirteen thousand six hundred eighty-five papers related to illicit drugs (5403), tobacco (4469), and alcohol (2137) use disorders and treatment were published between 1971 and 2017. The number of publications on Mindfulness and Digital medicine topics had the highest increase with more than 300% since 2003-2007 despite later presence than other methods. The number of papers on other non-pharmaceutical therapies (behavioral therapy, cognitive behavioral therapy, skills training or motivational interviewing) grew gradually, however, the growth rate was lower every 5-year period. The United States is the substance use disorder research hub of the world with the highest volume of publications (8232 or 60.2%) and total citations (252,935 or 65.2%), number of prolific authors (25 of top 30 or 83%) and institutions (24 of top 26 or 92%), formed the most international research partnerships (with 96 distinct countries). The international collaboration followed a pattern based on geographic proximity and cultural similarity. CONCLUSIONS: This study offers a comprehensive picture of the global trend of publications of substance use disorder. Findings suggest a need for research policy that supports the examination of interventions that culturally adhere to different local contexts to address substance use disorder in communities.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.064 | 0.094 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".