Efficacy of Serious Games in Healthcare Professions Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it