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Participação em saúde nas Américas: mapeamento bibliométrico da produção, impacto, visibilidade e colaboração

2023· article· pt· W4316469193 on OpenAlexaboutno aff
Frederico Viana Machado, Carla Michele Rech, Rodrigo Silveira Pinto, Wagner de Melo Romão, Manuelle Maria Marques Matias, Gabriele Carvalho de Freitas, Fernando Antônio Gomes Leles, Henrique Aniceto Kujawa

Bibliographic record

VenueCiência & Saúde Coletiva · 2023
Typearticle
Languagept
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesSciELOPolitical sciencePhilosophyScopusMEDLINE

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Bibliometrics, Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.030
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.019

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.

Opus teacher head0.070
GPT teacher head0.349
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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