The Effects of a Digital Game Simulator versus a Traditional Intervention on Paramedics’ Neonatal Resuscitation Performance
Bibliographic record
Abstract
Neonatal resuscitation is a skill set that comprises procedures, assessment, decision-making, communication, and teamwork. It is used in an emergency situation in the delivery room with the aim of supporting newborn infants who are not able to begin breathing on their own. Thus, healthcare providers need to refresh their neonatal resuscitation skills periodically, according to the Neonatal Resuscitation Program, to ensure that they can react quickly and effectively in emergency situations. The RETAIN digital game simulator was designed to enable healthcare providers to practice their neonatal resuscitation skills. To evaluate the effectiveness of this game in a laboratory setting, a randomized control trial sampled 42 paramedics who completed a pre-test, were randomly assigned to watch a traditional lecture video on the neonatal resuscitation procedure or to play a novel digital game simulation on the same topic, and then completed a following test. A two-way mixed ANOVA revealed a statistically significant improvement in paramedics' neonatal resuscitation performance over time, which did not differ between conditions. Thus, digital games can provide an enjoyable alternative to traditional practices in refreshing neonatal resuscitation knowledge.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".