Efficacy of Serious Games in Healthcare Professions Education
Bibliographic record
Abstract
SUMMARY STATEMENT: Serious games (SGs) are interactive and entertaining software designed primarily with an educational purpose. This systematic review synthesizes evidence from experimental studies regarding the efficacy of SGs for supporting engagement and improving learning outcomes in healthcare professions education. Randomized controlled trials (RCTs) published between January 2005 and April 2019 were included. Reference selection and data extraction were performed in duplicate, independently. Thirty-seven RCTs were found and 29 were included in random-effect meta-analyses. Compared with other educational interventions, SGs did not lead to more time spent with the intervention {mean difference 23.21 minutes [95% confidence interval (CI) = -1.25 to 47.66]}, higher knowledge acquisition [standardized mean difference (SMD) = 0.16 (95% CI = -0.20 to 0.52)], cognitive [SMD 0.08 (95% CI = -0.73 to 0.89)], and procedural skills development [SMD 0.05 (95% CI = -0.78 to 0.87)], attitude change [SMD = -0.09 (95% CI = -0.38 to 0.20)], nor behavior change [SMD = 0.2 (95% CI = -0.11 to 0.51)]. Only a small SMD of 0.27 (95% CI = 0.01 to 0.53) was found in favor of SGs for improving confidence in skills.
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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 | 0.014 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".