Impact of a Multifactorial Educational Training on the Management of Preterm Infants in the Central-Eastern European Region
Bibliographic record
Abstract
Background: Differences in management and outcomes of extremely preterm infants have been reported across European countries. Implementation of standardized guidelines and interventions within existing neonatal care facilities can improve outcomes of extremely preterm infants. This study evaluated whether a multifactorial educational training (MET) course in Vienna focusing on the management of extremely preterm infants had an impact on the management of extremely preterm infants in Central-Eastern European (CEE) countries. Methods: Physicians and nurses from different hospitals in CEE countries participated in a two-day MET in Vienna, Austria with theoretical lectures, bedside teaching, and simulation trainings. In order to evaluate the benefit of the workshops, participants had to complete pre- and post-workshop questionnaires, as well as follow-up questionnaires three and twelve months after the MET. Results: 162 participants from 15 CEE countries completed the two-day MET at our department. Less invasive surfactant administration (LISA) was only used by 39% (63/162) of the participants. After the MET, 80% (122/152) were planning to introduce LISA, and 66% (101/152) were planning to introduce regular simulation training, which was statistically significantly increased three and twelve months after the MET. Thirty-six percent and 57% of the participants self-reported improved outcomes three and twelve months after the MET, respectively. Conclusion: Our standardized training in Vienna promoted the implementation of different perinatal concepts including postnatal respiratory management using LISA as well as regular simulation trainings at the participants' home departments. Moreover, our MET contributed to dissemination of guidelines, promoted best-practice, and improved self-reported outcomes.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".