Academic Primer Series: Five Key Papers about Study Designs in Medical Education
Bibliographic record
Abstract
INTRODUCTION: A proper understanding of study design is essential to creating successful studies. This is also important when reading or peer reviewing publications. In this article, we aimed to identify and summarize key papers that would be helpful for faculty members interested in learning more about study design in medical education research. METHODS: The online discussions of the 2016-2017 Academic Life in Emergency Medicine Faculty Incubator program included a robust and vigorous discussion about education study design, which highlighted a number of papers on that topic. We augmented this list of papers with further suggestions by expert mentors. Via this process, we created a list of 29 papers in total on the topic of medical education study design. After gathering these papers, our authorship group engaged in a modified Delphi approach to build consensus on the papers that were most valuable for the understanding of proper study design in medical education. RESULTS: We selected the top five most highly rated papers on the topic domain of study design as determined by our study group. We subsequently summarized these papers with respect to their relevance to junior faculty members and to faculty developers. CONCLUSION: This article summarizes five key papers addressing study design in medical education with discussions and applications for junior faculty members and faculty developers. These papers provide a basis upon which junior faculty members might build for developing and analyzing studies.
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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 | 0.178 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.017 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.007 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 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; 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".