Examining the Representation of South Asian Populations in Substance Addiction Research: A Bibliometric Analysis From 2014 to 2024
Bibliographic record
Abstract
Substance addiction is a major global health concern, yet South Asian populations remain underrepresented in research, limiting understanding of how addiction affects these communities. Therefore, this study aimed to evaluate substance addiction research productivity in South Asian populations through a 10-year bibliometric analysis (2014-2024). A systematic PubMed search identified 1,320 research publications that met the inclusion criteria. Extracted data included annual publication counts, article type, five-year journal impact factor (JIF), citation counts, and country of publication. South Asian populations were examined both as a whole and as specific subgroups from Afghanistan, Bangladesh, Bhutan, India, the Maldives, Nepal, Pakistan, and Sri Lanka. Annual publications showed a significant upward trend (slope=3.73 studies/year, p=0.016), increasing from 124 in 2014 to 164 in 2024. The highest number of studies was conducted on the Indian population (n=873; 66.1%), which was also the only group to show a significant growth trend (slope=3.13 studies/year, p=0.03). Additionally, most publications were original research articles (58.9%), with a mean five-year JIF of 4.87 and an average of 30.5 citations per article. Populations from Afghanistan and India had the highest values for these metrics, while the rest remained underrepresented. Furthermore, India (n=494) and the United States (n=332) were the top countries of publication, together producing 63% of all research. Notably, six of the top 10 publishing countries were outside South Asia. In conclusion, research on substance addiction in South Asian populations has grown significantly over the past decade but remains heavily skewed toward India, with other groups underrepresented in both research quantity and quality. Through identifying these trends, this study highlights critical gaps and priorities for more equitable research.
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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 | 0.015 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.093 | 0.126 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".