Scientific production in primary health care in Latin American and Caribbean Countries (1980–2024): A web of science perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: To analyze the scientific production of primary care research in Latin American and Caribbean (LAC) countries from 1980 to 2024 and to provide recommendations for improvement. DESIGN: Observational, machine learning-based bibliometric study. DATA SOURCES: Review and research articles indexed in the Web of Science database. SELECTION OF STUDIES: Bibliometric analysis was performed on data from 33 LAC countries, retrieved from the Web of Science as of April 15, 2024. DATA EXTRACTION: For each record, data on the journal, year of publication, article title, abstract, keywords, authors, affiliations, countries, cited sources, cited first authors, and references were extracted for bibliometric and text mining analyses. We used a form of machine learning, Latent Dirichlet Allocation topic modeling, to identify the key topics of research. RESULTS: LAC countries contributed only 0.83% of the global literature on primary health care, with just 0.98% of this output comprising research and review articles. The majority of research originated from Brazil, Mexico, Colombia, and Chile, while many LAC countries produced little to no output. LAC countries frequently collaborated with the United States, Spain, Canada, and England. Research topics in the region predominantly focused on cancer, obesity, COVID-19, nutritional disorders, and food safety within the primary health care field. CONCLUSIONS: The findings highlight significant potential for growth in primary health care research in LAC countries. Strengthening individual and collective strategies to build research capacity and fostering collaborations with global academic networks are recommended to enhance research output and impact.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it