Investigación en salud en Honduras: una década de transformaciones, desafíos y fortalecimiento institucional sostenible
Bibliographic record
Abstract
Honduras has historically faced significant limitations to scientific development, with research spending below 0.1% of the gross domestic product and a university system focused almost exclusively on teaching. However, between 2007 and 2012, the Teasdale-Corti project (a joint undertaking of Canada and Honduras) marked a turning point by establishing the first academic master's degree in infectious and zoonotic diseases, a biomedical research laboratory, an ethics committee, and biosafety training programs. This process laid the groundwork for the creation of the Microbiology Research Institute. Over the last decade, these initiatives have trained numerous researchers, consolidated research groups in priority infectious diseases, and led to over 170 publications in indexed journals, representing about 12% of the scientific output of the National Autonomous University of Honduras. Likewise, the advent of the assistant researcher role allowed for a significant increase in academic output. Comparison with other Central American countries reveals a marked lag compared to Costa Rica and Panama, mainly explained by the low level of investment in research. Conversely, factors such as international collaboration, local leadership, and institutional integration have been decisive in sustaining progress. The Honduran experience demonstrates that, even in contexts of vulnerability, it is possible to build scientific capacities by combining a strategic vision, sustainable partnerships, and institutional commitment. Nevertheless, these achievements are still fragile and require future consolidation.
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".