Health and Human Rights Nexus: A Bibliometric Analysis
Bibliographic record
Abstract
This research aims to discover how human rights have been blended into health literature over time. The time frame of the reviewed literature was 1963 to February 2022. We retrieved the bibliometric dataset from Scopus. After compiling and processing the dataset, we finally got 5,458 documents on health and human rights issues. We used the open-source visualization software package Vos viewer 1.6.18 to process and analyze the data. The visualization focused on the top authors, most influential publications by citations, and most productive countries. The lexical network analysis shows that ‘public health’, ‘HIV/AIDS’, ‘mental health’, and ‘ethics’ are frequently associated words with ‘human rights’. The USA, UK, Canada, and Australia are the top four countries dominating the field. Collaboration among the developed nations is much higher than among world's developing countries. The University of Toronto, the University of British Columbia, and the University of Western Ontario are the top three organizations in Canada leading the research on health and human rights issues based on citations. However, if we consider publication's growth, the USA ranked first. Human rights have been perfectly blended in health literature, but a large gap exists in their spatial distribution and in making collaboration. A strong north-north and very weak south-south pattern in research collaboration are evident in visual effects that demand strong attention for future 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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.235 | 0.274 |
| 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) | 0.006 | 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".