Serious games, a game changer in teaching neonatal resuscitation? A review
Bibliographic record
Abstract
BACKGROUND: Neonatal healthcare professionals require frequent simulation-based education (SBE) to improve their cognitive, psychomotor and communication skills during neonatal resuscitation. However, current SBE approaches are resource-intensive and not routinely offered in all healthcare facilities. Serious games (board and computer based) may be effective and more accessible alternatives. OBJECTIVE: To review the current literature about serious games, and how these games might improve knowledge retention and skills in neonatal healthcare professionals. METHOD: Literature searches of PubMed, Google Scholar, Cochrane Central Register of Controlled Trials, CINAHL, Web of Science and EMBASE databases were performed to identify studies examining serious games in neonatology. All games, such as board games, tabletop games, video games, screen-based simulators, tabletop simulators and virtual reality games were included. RESULTS: Twelve serious games were included in this review (four board games, five video games and three virtual reality games). Overall, knowledge improvement was reported for the RETAIN (REsuscitationTrAINing for healthcare professionals) board game (10% increase in knowledge retention) and The Neonatology Game (4.15 points higher test score compared with control). Serious games are increasingly incorporated into Nursing and Medical School Curriculums to reinforce theoretical and practical learning. CONCLUSIONS: Serious games have the potential to improve healthcare professionals' knowledge, skills and adherence to the resuscitation algorithm and could enhance access to SBE in resource-intensive and resource-limited areas. Future research should examine important clinical outcomes in newborn infants.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".