Impact of in situ simulation training on quality of postnatal stabilization and resuscitation—a before-and-after, non-controlled quality improvement study
Bibliographic record
Abstract
This study aimed to evaluate the impact of in situ simulation-based training on quality indicators of patient care at a level IV neonatal intensive care unit. A before-and-after, non-controlled quality improvement study was performed at the Division of Neonatology, Medical University of Graz. The educational intervention comprised a period of 4 months, with structured in situ simulation training delivered regularly for neonatal providers and nurses in interprofessional teams. The primary study outcome was the quality of non-technical skills and team interaction during actual postnatal stabilization and resuscitation. This was assessed using video recording during two 2-month observational phases before (pre-training) and after the educational intervention (post-training). Delivery room video recordings were assessed by two external, blinded neonatologists using the Anaesthetists' Non-Technical Skills (ANTS) score. Furthermore, we collected clinical patient data from video-recorded neonates during the pre- and post-training periods, and training participants' individual knowledge of neonatal resuscitation guidelines was assessed using a before- and after-questionnaire. A total of 48 healthcare professionals participated in 41 in situ simulation trainings. The level of non-technical skills and team interaction was already high in the pre-training period, and it did not further improve afterwards. Nonetheless, we observed a significant increase in the teamwork event "evaluation of plans" (0.5 [IQR 0.0-1.0] versus 1.0 [1.0-2.0], p = 0.049). Following the educational intervention, training participants' knowledge of neonatal resuscitation guidelines significantly improved, although there were no differences in secondary clinical outcome parameters. CONCLUSION: We have successfully implemented a neonatal in situ simulation training programme. The observed improvement in one teamwork event category in the post-training period demonstrates the effectiveness of the training curriculum, while also showing the potential of in situ simulation training for improving postnatal care and, ultimately, patient outcome. WHAT IS KNOWN: • In situ simulation-based training is conducted in the real healthcare environment, thus promoting experiential learning which is closely aligned with providers' actual work. • In situ simulation-based training may offer an additional benefit for patient outcomes in comparison to other instructional methodologies. WHAT IS NEW: • This observational study investigated translational patient outcomes in preterm neonates before and after delivery of high-frequency in situ simulation-based training at a level IV neonatal intensive care unit. • There was a significant increase in the frequency of one major teamwork event following the delivery of in situ simulation-based training, indicating a notable improvement in the non-technical skills domain, which is closely linked to actual team performance.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".