Effectiveness of serious games and impact of design elements on engagement and educational outcomes in healthcare professionals and students: a systematic review and meta-analysis protocol
Bibliographic record
Abstract
INTRODUCTION: Serious games (SGs) are interactive and entertaining digital software with an educational purpose. They engage the learner by proposing challenges and through various design elements (DEs; eg, points, difficulty adaptation, story). Recent reviews suggest the effectiveness of SGs in healthcare professionals' and students' education is mixed. This could be explained by the variability in their DEs, which has been shown to be highly variable across studies. The aim of this systematic review is to identify, appraise and synthesise the best available evidence regarding the effectiveness of SGs and the impact of DEs on engagement and educational outcomes of healthcare professionals and students. METHODS AND ANALYSIS: A systematic search of the literature will be conducted using a combination of medical subject headings terms and keywords in Cumulative Index of Nursing and Allied Health, Embase, Education Resources Information Center, PsycInFO, PubMed and Web of Science. Studies assessing SGs on engagement and educational outcomes will be included. Two independent reviewers will conduct the screening as well as the data extraction process. The risk of bias of included studies will also be assessed by two reviewers using the Effective Practice and Organisation of Care criteria. Data regarding DEs in SGs will first be synthesised qualitatively. A meta-analysis will then be performed, if the data allow it. Finally, the quality of the evidence regarding the effectiveness of SGs on each outcome will be assessed using the Grading of Recommendations Assessment, Development and Evaluation approach. ETHICS AND DISSEMINATION: As this systematic review only uses already collected data, no Institutional Review Board approval is required. Its results will be submitted in a peer-reviewed journal by the end of 2018. PROSPERO REGISTRATION NUMBER: CRD42017077424.
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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 | 0.078 | 0.105 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.029 | 0.040 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.004 |
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".