Game-based versus traditional case-based learning: comparing effectiveness in stroke continuing medical education.
Bibliographic record
Abstract
OBJECTIVE: To evaluate family physicians' enjoyment of and knowledge gained from game-based learning, compared with traditional case-based learning, in a continuing medical education (CME) event on stroke prevention and management. DESIGN: An equivalence trial to determine if game-based learning was as effective as case-based learning in terms of attained knowledge levels. Game questions and small group cases were developed. Participants were randomized to either a game-based or a case-based group and took part in the event. SETTING: Ontario provincial family medicine conference. PARTICIPANTS: Thirty-two family physicians and 3 senior family medicine residents attending the conference. INTERVENTION: Participation in either a game-based or a case-based CME learning group. MAIN OUTCOME MEASURES: Scores on 40-item immediate and 3-month posttests of knowledge and a satisfaction survey. RESULTS: Results from knowledge testing immediately after the event and 3 months later showed no significant difference in scoring between groups. Participants in the game-based group reported higher levels of satisfaction with the learning experience. CONCLUSION: Games provide a novel way of organizing CME events. They might provide more group interaction and discussion, as well as improve recruitment to CME events. They might also provide a forum for interdisciplinary CME. Using games in future CME events appears to be a promising approach to facilitate participant learning.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".