Global Scientific Production on Illicit Drug Addiction: A Two-Decade Analysis
Bibliographic record
Abstract
AIMS: Addiction science has made great progress in the past decades. We conducted a scientometric study in order to quantify the number of publications and the growth rate globally, regionally, and at country levels. METHODS: In October 2015, we searched the Scopus database using the general keywords of addiction or drug-use disorders combined with specific terms regarding 4 groups of illicit drugs - cannabis, opioids, cocaine, and other stimulants or hallucinogens. All documents published during the 20-year period from 1995 to 2014 were included. RESULTS: A total of 95,398 documents were retrieved. The highest number of documents were on opioids, both globally (60.1%) and in each of 5 continents. However, studies on cannabis showed a higher growth rate in the last 5-year period of the study (2010-2014). The United States, the United Kingdom, Germany, Canada, Australia, France, Spain, Italy, China, and Japan - almost all studies were from high-income countries - occupied the top 10 positions and produced 81.4% of the global science on drug addiction. CONCLUSION: As there are important socio-cultural differences in the epidemiology and optimal clinical care of addictive disorders, it is suggested that low- and more affected middle-income countries increase their capacity to conduct research and disseminate the knowledge in this field.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.122 | 0.216 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".