Progress Tests in Health Professions Education
Bibliographic record
Abstract
Assessment and evaluation are crucial in education, particularly for measuring students' cognitive skills and proficiency. Multiple-choice question (MCQ) tests are a common method in health sciences, requiring high validity and reliability.Developmental examinations, composed of MCQs, regularly track students' progress and cognitive levels. Introduced in 1971 at the University of Missouri-Kansas City School of Medicine and later adopted by Maastricht and McMaster Universities, these exams now include both written and online formats.This section compares developmental exams worldwide, focusing on the Netherlands, Canada, Germany, Austria, the UK, Brazil, and Turkey. Key aspects include question type, number, duration, frequency, mode of answering, and evaluation methods. Although not specific performance tools, these exams are vital for assessing academic success, providing comprehensive curriculum coverage and longitudinal cognitive performance assessment, essential for tracking students' progress toward learning objectives
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".