Participation in health in the Americas: Bibliometric mapping of production, impact, visibility and collaboration
Bibliographic record
Abstract
Abstract Participation in health has generated a large number of publications around the world. In order to know the specificities of this production in the Americas, a bibliometric analysis of articles in English, Spanish and Portuguese was carried out. Searches were carried out in the VHL, PubMed, SCOPUS, WOS and SciELO, consolidating a database with 641 references. With the help of the VOSviewer software, we analyzed citation patterns, co-authorship and the chronological distribution by countries and languages. It was possible to verify the growth of production, the quantitative relevance and the impact of the different countries. The analysis indicated that the USA concentrates the largest number of citations and Brazil, despite being the first in number of publications, is the third in number of citations. The same occurs with Brazilian journals that, with the largest number of articles, fall in the ranking of the most cited. The co-authorship analysis indicated that the University of Toronto, Fiocruz and Harvard University have the most formal collaborations with other organizations. We conclude that there are inequalities of impact, visibility and internationalization in this field, indicating obstacles to scientific development and health policies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.063 | 0.111 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".