Where does Hispanic Latin America stand in biomedical and life sciences literature production compared with other countries?
Bibliographic record
Abstract
Objectives: to provide objective quantitative data about medical-related scientific production in Hispanic Latin America compared to different regions and identify demographic and political variables that could improve research. Study design: This is an analytical, observational, cross-section bibliometric study about all fields of medical-related scientific production over five years in different regions and its relationship with demographic and political variables that could impact research and the health system quality. Methods: Data on the total scientific production of all Hispanic Latin American countries and other countries representing almost 90% of mundial publications between 2017 and 2021 were retrieved from the PubMed database. Demographic and political data were obtained from open online databases. Counts of publications were rationed to population and analyzed with all other demographic, region, and language variables, using univariate Poisson regression and negative binomial regression (for over-disperse variables) analysis. Multivariate negative binomial regression was used to analyze the combined effect of variables related to the healthcare and research Sectors. Results: Hispanic Latin America increased yearly from 29,445 publications in 2017 to 47,053 in 2021. This cumulative growth of almost 60% exceeded the 36% increment in all countries' publications and was only below that of Russia and China, which grew 92% and 87%, respectively. Negative binomial regression showed that the percentage of gross income dedicated to research (IRR 2.036, 95% CI: 1.624, 2.553, p< .001), life expectancy at birth (IRR 1.444, 95% CI: 1.338, 1.558, p< .001), and the number of medical doctors per inhabitant (IRR 1.581, 95% CI: 1.17, 2.13, p = .003) positively impacted scientific production. A higher mortality associated with chronic diseases between ages 30 and 70 (IRR 0.782, 95% CI: 0.743 0.822, p< .001) and a lower population with access to medicine (IRR 0.960, 95% CI: 0.933, 0.967, p< .001) were found to impact scientific production negatively. Hispanic Latin American countries published less than 20% of those with English as their native language (p< .001). Conclusion: Hispanic Latin America has increased the gross number of publications by almost 60 % from 2017 to 2021. However, the number of publications per 100,000 inhabitants is still low compared to other countries. Our analysis highlights that this may be related to lower GDP, research investment, and less healthcare system quality.
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How this classification was reachedexpand
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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".