Support for and aspects of use of educational games in family medicine and internal medicine residency programs in the US: a survey
Bibliographic record
Abstract
BACKGROUND: The evidence supporting the effectiveness of educational games in graduate medical education is limited. Anecdotal reports suggest their popularity in that setting. The objective of this study was to explore the support for and the different aspects of use of educational games in family medicine and internal medicine residency programs in the United States. METHODS: We conducted a survey of family medicine and internal medicine residency program directors in the United States. The questionnaire asked the program directors whether they supported the use of educational games, their actual use of games, and the type of games being used and the purpose of that use. RESULTS: Of 434 responding program directors (52% response rate), 92% were in support of the use of games as an educational strategy, and 80% reported already using them in their programs. Jeopardy like games were the most frequently used games (78%). The use of games was equally popular in family medicine and internal medicine residency programs and popularity was inversely associated with more than 75% of residents in the program being International Medical Graduates. The percentage of program directors who reported using educational games as teaching tools, review tools, and evaluation tools were 62%, 47%, and 4% respectively. CONCLUSIONS: Given a widespread use of educational games in the training of medical residents, in spite of limited evidence for efficacy, further evaluation of the best approaches to education games should be explored.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".