Assessment of Global Health Education: The Role of Multiple-Choice Questions
Bibliographic record
Abstract
Introduction: The standardization of global health education and assessment remains a significant issue among global health educators. This paper explores the role of multiple choice questions (MCQs) in global health education: whether MCQs are appropriate in written assessment of what may be perceived to be a broad curriculum packed with fewer facts than biomedical science curricula; what form the MCQs might take; what we want to test; how to select the most appropriate question format; the challenge of quality item-writing; and, which aspects of the curriculum MCQs may be used to assess. Materials and Methods: The Medical School for International Health (MSIH) global health curriculum was blue-printed by content experts and course teachers. A 30-question, 1-h examination was produced after exhaustive item writing and revision by teachers of the course. Reliability, difficulty index and discrimination were calculated and examination results were analyzed using SPSS software. Results: Twenty-nine students sat the 1-h examination. All students passed (scores above 67% - in accordance with University criteria). Twenty-three (77%) questions were found to be easy, 4 (14%) of moderate difficulty, and 3 (9%) difficult (using examinations department difficulty index calculations). Eight questions (27%) were considered discriminatory and 20 (67%) were non-discriminatory according to examinations department calculations and criteria. The reliability score was 0.27. Discussion: Our experience shows that there may be a role for single-best-option (SBO) MCQ assessment in global health education. MCQs may be written that cover the majority of the curriculum. Aspects of the curriculum may be better addressed by non-SBO format MCQs. MCQ assessment might usefully complement other forms of assessment that assess skills, attitude and behavior. Preparation of effective MCQs is an exhaustive process, but high quality MCQs in global health may serve as an important driver of learning.
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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 | 0.050 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".