e-Learning Evaluation Framework and Tools for Global Health and Public Health Education: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: There has been a significant increase in the use of e-learning for global and public health education recently, especially following the COVID-19 pandemic. e-Learning holds the potential to offer equal opportunities, overcoming barriers like physical limitations and training costs. However, its effectiveness remains debated, with institutions unprepared for the sudden shift during the pandemic. To effectively evaluate the outcomes of e-learning, a standardized and rigorous approach is necessary. However, the existing literature on this subject often lacks standardized assessment tools and theoretical foundations, leading to ambiguity in the evaluation process. Consequently, it becomes imperative to identify a clear theoretical foundation and practical approach for evaluating global and public health e-learning outcomes. OBJECTIVE: This protocol for a scoping review aims to map the state of e-learning evaluation in global and public health education to determine the existing theoretical evaluation frameworks, methods, tools, and domains and the gaps in research and practice. METHODS: The scoping review will be conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. The initial search was performed in PubMed, Education Resource Information Center, Web of Science, and Scopus to identify peer-reviewed articles that report on the use of evaluation and assessment for e-learning training. The search strings combined the concepts of e-learning, public health, and health science education, along with evaluation and frameworks. After the initial search, a screening process will be carried out to determine the relevance of the identified studies to the research question. Data related to the characteristics of the included studies, the characteristics of the e-learning technology used in the studies, and the study outcomes will be extracted from the eligible articles. The extracted data will then undergo a structured, descriptive, quantitative, and qualitative content analysis to synthesize the information from the selected studies. RESULTS: Initial database searches yielded a total of 980 results. Duplicates have been removed, and title and abstract screening of the 805 remaining extracted articles are underway. Quantitative and qualitative findings from the reviewed articles will be presented to answer the study objective. CONCLUSIONS: This scoping review will provide global and public health educators with a comprehensive overview of the current state of e-learning evaluation. By identifying existing e-learning frameworks and tools, the findings will offer valuable guidance for further advancements in global and public health e-learning evaluation. The study will also enable the creation of a comprehensive, evidence-based e-learning evaluation framework and tools, which will improve the quality and accountability of global health and public health education. Ultimately, this will contribute to better health outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/49955.
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 | 0.175 | 0.153 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.070 | 0.017 |
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".