Using Simulation-Based Experiential Learning to Increase Students’ Ability to Analyze Increasingly Complex Global Health Challenges: A Mixed Methods Study
Bibliographic record
Abstract
Abstract Traditional didactic teaching approaches fall short of adequately supporting diverse student learning styles. Complementing didactic teaching approaches with simulation-based experiential learning has shown promise in bridging the gap between theoretical knowledge and practical application. However, few studies have rigorously examined the outcomes of this approach in global health education and training. This study aims to evaluate the impact of the World Health Organization World Health Assembly Simulation (WHA Sim), a simulation-based experiential learning platform designed to enhance the application of students’ knowledge and skills in practical settings relating to global health governance. We employed a sequential mixed-method study between September 2022 and July 2023, starting with an anonymous survey among undergraduate students in the Faculty of Health at York University, in order to evaluate self-reported quantitative metrics related to students’ understanding of simulation-based learning prior to the WHA Sim. We also conducted qualitative interviews among participants of the WHA Sim from diverse health disciplines, aiming to capture multi-disciplinary perspectives. Data was analyzed using simple descriptive statistics for the quantitative data and a framework analysis for qualitative data. Among 39 survey respondents, 18 were interviewed. The WHA Sim bolstered a wide array of skills, including research capabilities, critical analysis, time management, and organizational effectiveness. Participants also reported improvements in effective interpersonal communication, public speaking, networking, and solution-driven dialogues. Interpersonal skills like collaboration and leadership were notably improved. Moreover, the simulation provided an enhanced understanding of complex issues and offered a fertile ground for career preparation, filling existing knowledge gaps more effectively than traditional learning environments. Findings demonstrate the value of simulation-based experiential learning among undergraduate students, which illustrates how the WHA Sim can serve as an effective learning tool within global health education and training.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".