A RIME Perspective on the Quality and Relevance of Current and Future Medical Education Research
Bibliographic record
Abstract
In this article, the authors consider the quality and relevance of current and future medical education research by (1) presenting a framework for medical education research and reviewing basic principles of "good" empirical work, (2) extending the discussion of principles to "best practices," (3) considering the distinctive features of medical education that present challenges to the researcher, and (4) discussing opportunities for expanding the scope and influence of medical education research. Their audience is intended to be clinicians involved in education, deans and associate deans who create and direct educational curricula and processes, and those from offices critical to the educational mission such as admissions, student services and financial aid, as well as medical education researchers. The authors argue that the quality and relevance of current work can be enhanced when research is situated within a general framework and questions are asked that are based on literature and theory and push the field toward new knowledge. Obviously methods and designs must be appropriate and well-executed and sufficient data must be gathered. Multiple studies are highlighted that showcase the rigor and creativity associated with excellent quality work. However, good research is not without its challenges, most notably short timelines and the need to work within an ever-changing real-life educational environment. Most important, the field of medical education research has many opportunities to increase its impact and advance its quest to study important learners' behaviors and patients' outcomes. Programs to train and collaborate with clinical and administrative colleagues, as well as researchers in other fields, have great potential to improve the quality of research in the field.
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 | 0.520 | 0.601 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.011 | 0.108 |
| Scholarly communication | 0.052 | 0.055 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.023 | 0.031 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier 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".