Print, Play, and Learn: Cataloging Card and Board Games for Medical Education From 1980 to 2025
Bibliographic record
Abstract
Background Card and board games are increasingly described in the medical education literature, but games in the literature are not always available for use by educators. Since the last survey of games for medical education, new funding and distribution technologies have reduced barriers to sharing games, and we hypothesize that more games are available than described in the literature and that new technologies have contributed to this availability. We aim to describe the current landscape of games beyond the literature to facilitate their use and study. Methods For this study, we curated a list of sites where games are available for download and/or purchase by searching for sites associated with the games from an earlier review. We searched these sites over a three-year period to build a catalog of games published in or before 2024 that were designed for physicians and/or physician-track learners. We described the audiences, content areas, technologies used for distribution, and other details about the games. Results We identified 224 games, of which 87 met the inclusion criteria. The number of games increased year-over-year from 2013-2019, and the peak year was 2023. Popular game topics included infectious disease (n=15), anatomy (n=11), neurology (n=10), pediatrics (n=10), and emergency medicine (n=9). Games were often shared using printable files (34) and print-on-demand services (23). Only 31 (36%) games had an associated academic publication. Conclusions The number of medical education games has substantially increased in the last decade, facilitated by the adoption of print-on-demand and the sharing of printable files. Gaps in content area and target audience still remain, and we encourage educators to employ funding and distribution technologies to facilitate sharing games more widely.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".