Exploring socio-economic dimensions in HIV research: a comprehensive bibliometric analysis (1992–2024)
Bibliographic record
Abstract
The socio-economic burden of HIV infection remains a critical global health concern. This study was conducted to perform a comprehensive bibliometric analysis of the socio-economic burden of HIV infection, highlighting research trends, collaboration networks, and the evolving focus on social determinants of health over the past 32 years. A systematic search was conducted in Scopus and Web of Science Core Collection databases, covering publications from 1992 to 2024. The analysis was performed using RStudio and Biblioshiny, focusing on 1,054 studies from 422 publications. This study revealed a steady annual growth rate of 16.72% in publications on the socio-economic burden of HIV from 1992 to 2024, with the USA and Canada leading in contributions. The University of Toronto emerged as the top institution, while 'social determinants of health' and 'HIV infections' were identified as pivotal research themes. Collaboration networks were predominantly among high-income countries, with limited engagement from high-burden regions like sub-Saharan Africa. Key journals, such as AIDS and Behavior, were identified as central to advancing the field. Thematic analysis highlighted a shift from biomedical to socio-economic factors, emphasizing the need for equitable global collaboration and research addressing disparities in HIV management. This comprehensive analysis provides valuable insights into the evolving landscape of HIV socio-economic burden research, emphasizing the need for increased collaboration with high-burden regions and a continued focus on addressing social determinants of health in HIV management.
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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 | 0.024 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.206 | 0.271 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".