Making Medical Education Courses Visible: Theory-Based Development of a National Database
Bibliographic record
Abstract
Background: Medical education has undergone professionalization during the last decades, and internationally, educators are trained in specific medical education courses also known as "train the trainer" courses. As these courses have developed organically based on local needs, the lack of a general structure and terminology can confuse and hinder educators' information and development. The first aim of this study was to conduct a national search, analyze the findings, and provide a presentation of medical education courses based on international theoretical frameworks to support Swiss course providers and educators searching for courses. The second aim was to provide a blueprint for such a procedure to be used by the international audience. Objective: In this study, we devised a scholarly approach to sorting and presenting medical education courses to make their content accessible to medical educators. This approach is presented in detailed steps and our openly available exemplary database to make it serve as a blueprint for other settings. Methods: Following our constructivist paradigm, we examined content from medical education courses using a theory-informed inductive data approach. Switzerland served as an example, covering 4 languages and different approaches to medical education. Data were gathered through an online search and a nationwide survey with course providers. The acquired data and a concurrently developed keyword system to standardize course terminology are presented using Obsidian, a software that shows data networks. Results: Our iterative search included several strategies (web search, survey, provider enquiry, and snowballing) and yielded 69 courses in 4 languages, with varying terminology, target audiences, and providers. The database of courses is interactive and openly accessible. An open-access template database structure is also available. Conclusions: This study proposes a novel method for sorting and visualizing medical education courses and the competencies they cover to provide an easy-to-use database, helping medical educators' practical and scholarly development. Notably, our analysis identified a specific emphasis on undergraduate teaching settings, potentially indicating a gap in postgraduate educational offerings. This aspect could be pivotal for future curriculum development and resource allocation. Our method might guide other countries and health care professions, offering a straightforward means of cataloging and making information about medical education courses widely available and promotable.
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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 | 0.107 | 0.281 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".