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Record W4402750888 · doi:10.1007/s00431-024-05781-3

Impact of in situ simulation training on quality of postnatal stabilization and resuscitation—a before-and-after, non-controlled quality improvement study

2024· article· en· W4402750888 on OpenAlexaff
Lukas P. Mileder, Nariae Baik‐Schneditz, Jasmin Pansy, Bernhard Schwaberger, Wolfgang Raith, Alexander Avian, Georg M. Schmölzer, Peter Wöckinger, Gerhard Pichler, Berndt Urlesberger

Bibliographic record

VenueEuropean Journal of Pediatrics · 2024
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsRoyal Alexandra HospitalNorthern Alberta Institute of TechnologyUniversity of Alberta
FundersMedizinische Universität GrazKarl-Franzens-Universität Graz
KeywordsMedicineResuscitationQuality (philosophy)Quality managementEmergency medicineMedical emergencyIntensive care medicineOperations management

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.421
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2024
Admission routes1
Has abstractyes

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