A Web-Based Program About Sustainable Development Goals Focusing on Digital Learning, Digital Health Literacy, and Nutrition for Professional Development in Ethiopia and Rwanda: Development of a Pedagogical Method
Bibliographic record
Abstract
BACKGROUND: East African countries face significant societal challenges related to sustainable development goals but have limited resources to address these problems, including a shortage of nutrition experts and health care workers, limited access to physical and digital infrastructure, and a shortage of advanced educational programs and continuing professional development. OBJECTIVE: This study aimed to develop a web-based program for sustainable development with a focus on digital learning, digital health literacy, and child nutrition, targeting government officials and decision-makers at nongovernmental organizations (NGOs) in Ethiopia and Rwanda. METHODS: A web-based program-OneLearns (Online Education for Leaders in Nutrition and Sustainability)-uses a question-based learning methodology. This is a research-based pedagogical method developed within the open learning initiative at Carnegie Mellon University, United States. Participants were recruited during the fall of 2020 from ministries of health, education, and agriculture and NGOs that have public health, nutrition, and education in their missions. The program was conducted during the spring of 2021. RESULTS: Of the 70 applicants, 25 (36%) were selected and remained active throughout the entire program and filled out a pre- and postassessment questionnaire. After the program, of the 25 applicants, 20 (80%, 95% CI 64%-96%) participants reported that their capacity to drive change related to the sustainable development goals as well as child nutrition in their organizations had increased to large extent or to a very large extent. Furthermore, 17 (68%, 95% CI 50%-86%) and 18 (72%, 95% CI 54%-90%) participants reported that their capacity to drive change related to digital health literacy and digital learning had increased to a large extent and to a very large extent, respectively. CONCLUSIONS: Digital learning based on a question-based learning methodology was perceived as a useful method for increasing the capacity to drive change regarding sustainable development among government officials and decision-makers at NGOs in Ethiopia and Rwanda.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".