The RETAIN Simulation-Based Serious Game—A Review of the Literature
Bibliographic record
Abstract
Background: Each year, over 13 million babies worldwide need help to breathe at birth. While guidelines recommend the Neonatal Resuscitation Program course, medical errors remain common. Frequent simulation training and assessment is needed to address this competence gap; however, alternative approaches are needed to overcome barriers to access. The RETAIN (REsuscitation TrAINing) simulation-based serious game (Retain Labs Medical Inc., Edmonton, AB, Canada) may provide a solution to supplement traditional training. This paper aims to review the available evidence about RETAIN for improving neonatal resuscitation education. 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 the RETAIN serious game for neonatal resuscitation training. All of the studies describing the RETAIN board game and computer game were included. Results: Three papers and one conference proceeding were identified. Two studies described the RETAIN board game, and two studies described the RETAIN computer game. RETAIN was reported as usable and clinically relevant. RETAIN also improved knowledge of neonatal resuscitation by 12% and functioned as a summative assessment. Further, performance on RETAIN was moderated by players’ self-reported mindset. Conclusion: RETAIN can be used for the training and assessment of experienced neonatal resuscitation providers. Further studies are needed to understand the effectiveness of RETAIN to (i) improve other cognitive and non-cognitive skills, (ii) in diverse populations of neonatal resuscitation providers, (iii) in comparison to current standard training approaches, and (iv) in improving clinical outcomes in the delivery room.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".