Multilingual Global E-Learning Pediatric Endocrinology and Diabetes Curriculum for Front Line Health Care Providers in Resource-Limited Countries: Development Study
Bibliographic record
Abstract
BACKGROUND: Electronic learning (e-learning) is a widely accessible, low-cost option for learning remotely in various settings that allows interaction between an instructor and a learner. OBJECTIVE: We describe the development of a free and globally accessible multilingual e-learning module that provides education material on topics in pediatric endocrinology and diabetes and that is intended for first-line physicians and health workers but also trainees or medical specialists in resource-limited countries. METHODS: As complements to concise chapters, interactive vignettes were constructed, exemplifying clinical issues and pitfalls, with specific attention to the 3 levels of medical health care in resource-limited countries. The module is part of a large e-learning portal, ESPE e-learning, which is based on ILIAS (Integriertes Lern-, Informations- und Arbeitskooperations-System), an open-source web-based learning management system. Following a review by global experts, the content was translated by native French, Spanish, Swahili, and Chinese-speaking colleagues into their respective languages using a commercial web-based translation tool (SDL Trados Studio). RESULTS: Preliminary data suggest that the module is well received, particularly in targeted parts of the world and that active promotion to inform target users is warranted. CONCLUSIONS: The e-learning module is a free globally accessible multilingual up-to-date tool for use in resource-limited countries that has been utilized thus far with success. Widespread use will require dissemination of the tool on a global scale.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".