A conceptual framework of game-informed principles for health professions education
Bibliographic record
Abstract
Games have been used for training purposes for many years, but their use remains somewhat underdeveloped and under-theorized in health professional education. This paper considers the basis for using serious games (games that have an explicit educational purpose) in health professional education in terms of their underlying concepts and design principles. These principles can be understood as a series of game facets: competition and conflict, chance and luck, experience and performance, simulation and make-believe, tactics and strategies, media, symbols and actions, and complexity and difficulty. Games are distinct and bound in ways that other health professional education activities are not. The differences between games and simulation can be understood in terms of the interconnected concepts of isomorphism (convergence with real-world practice) and anisomorphism (divergence from real-world practice). Gaming facets can extend the instructional design repertoire in health professional education.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".