Implementation of the Epi Training Kit: Impact Report
Bibliographic record
Abstract
This report presents the results of the implementation of the Epi Training Kit (EpiTKit): An open-access online training strategy in infectious disease modeling and public health data science that aims to strengthen regional capacities by providing Spanish-language educational materials tailored to Latin America and the Caribbean with a gender-inclusive approach. Developed by Pontificia Universidad Javeriana as part of the Epiverse TRACE LAC project, EpiTKit seeks to enhance learning opportunities in the region. The EpiTKit MOOC was offered asynchronously on the edX Edge platform of Pontificia Universidad Javeriana from October 15 to December 20, covering 10 learning units on Data Science in Public Health and the Modeling of Infectious Diseases. A total of 2,208 participants from 27 countries, including 18 from Latin America and the Caribbean, enrolled in the course. Of these, 1,570 participants (71%) accessed the content, and 577 participants (25%) successfully completed and passed the course with an average score above 80% on assessments. These results highlight EpiTKit’s impact in expanding access to high-quality training and strengthening essential skills for public health professionals in the region.
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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 | 0.029 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.011 |
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".