The globalization of addiction research: Capacity-building mechanisms and selected examples
Bibliographic record
Abstract
Over the past decade, the amount and variety of addiction research around the world has increased substantially. Researchers in Australia, Canada, United Kingdom, United States, and western Europe have significantly contributed to knowledge about addiction and its treatment. However, the nature and context of substance use disorders and the populations using drugs are far more diverse than is reflected in studies done in Western cultures. To stimulate new research from a diverse set of cultural perspectives, the National Institute on Drug Abuse (NIDA) has promoted the development of addiction research capacity and skills around the world for over 25 years. This review will describe the programs NIDA has developed to sponsor international research and research fellows and will provide some examples of the work NIDA has supported. NIDA fellowships have allowed 496 individuals from 96 countries to be trained in addiction research. The United Arab Emirates and Saudi Arabia have recently developed funding to support addiction research to study, with advice from NIDA, the substance use disorder problems that affect their societies. Examples from Malaysia, Tanzania, Brazil, Russian Federation, Ukraine, Republic of Georgia, Iceland, China, and Vietnam are used to illustrate research being conducted with NIDA support. Health services research, collaboratively funded by the U.S. National Institutes of Health and Department of State, addresses a range of addiction service development questions in low- and middle-income countries. Findings have expanded the understanding of addiction and its treatment, and are enhancing the ability of practitioners and policy makers to address substance use disorders.
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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 | 0.058 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".