Potential benefits and challenges of simulation-based neonatal resuscitation competition: A survey analysis of provincial competition in China
Bibliographic record
Abstract
Background: Simulation-based neonatal resuscitation training has been implemented worldwide with good educational and clinical results. Simulation-based competition (SBC), as an innovative derivative of neonatal resuscitation training, has been practiced recently but its potential effectiveness and challenges of competition are rarely studied. We tested the hypothesis that after SBC, participants could improve compliance with NRP® algorithm and teamwork, achieve lower stress and higher confidence in neonatal resuscitation. Methods: In February 2023, 108 health care providers in 27 teams from different regional centres participated in provincial SBC. Each team consisted of 4 members (NICU physician [lead], NICU nurse, midwife and obstetrician). The teams were to complete a resuscitation scenario (16 min) and their performance was evaluated. All participants were encouraged to take part in a post-resuscitation questionnaire survey voluntarily immediately after the scenarios finished. Demographic characteristics and questionnaire results of participants were collected, including the confidence and perceived stress levels before and after the competition. Results: < 0.001). The confidence level did not change before and after the competition, whereas stress was reduced after the competition. Conclusions: Participants in SBC might be benefited with improved compliance with NRP® algorithm, technical skills and teamwork. However, the impact, influence and sustainability of these benefits are uncertain. Further research is needed to explore ways to improve self-confidence and decrease stress in neonatal resuscitation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".