Agile Application of Digital Health Interventions during the COVID-19 Refugee Response
Bibliographic record
Abstract
Annals of Global Health is a peer-reviewed, fully open access, online journal dedicated to publishing high quality articles dedicated to all aspects of global health. The journal's mission is to advance global health, promote research, and foster the prevention and treatment of disease worldwide. Its goals are to improve the health and well-being of all people, advance health equity, and promote wise stewardship of the earth's environment. The latest journal impact factor is 2.90. Annals of Global Health is supported by the Program for Global Public Health and the Common Good at Boston College. It was founded in 1934 by the Icahn School of Medicine at Mount Sinai as the Mount Sinai Journal of Medicine. It is a partner journal of the Consortium of Universities for Global Health. From time to time, Annals of Global Health publishes Special Collections, a series of articles organized around a common theme in global health. Recent Special Collections have included "Local evidence and strategies in addressing NCDs Non-Communicable Diseases in Tanzania", "Universal Health Coverage through Integrated Care", and "The Minderoo-Monaco Commission on Plastics and Human Health". Global health workers interested in developing a Special Collection are strongly encouraged to contact the Managing Editor in advance to discuss the project.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".