Neonatal resuscitation workshop for trainees in standardized medical residency training—a pilot practice in Shenzhen, China
Bibliographic record
Abstract
Background Neonatal resuscitation is an important skillset for clinicians attending deliveries. Accredited neonatal resuscitation training is not obligatory in most training centers of standardized medical residency programs before 2022 in China. We investigated the feasibility and effectiveness of neonatal resuscitation simulation training (neo-RST) in residents in Shenzhen, China. Methods Four two-day neo-RST workshops were conducted in the University of Hong Kong-Shenzhen Hospital and Shenzhen Health Capacity Building and Continuing Education Center in 2020–2021. The workshops had Neonatal Resuscitation Program (NRP)® update, skill stations and simulation practice with debriefing. Each participant had the integrated skill station assessment (ISSA) at the end of workshop. Participants of workshops included residents of different disciplines and health care providers (HCPs) of neonatal and obstetrical departments. We compared demographic characteristics, neonatal resuscitation knowledge before training, ISSA overall and categorical scores on skill sets between residents and HCPs. Results In 2020–2021, 4 neo-RST workshops were conducted with 48 residents and 48 HCPs. The residents group had less working experience, less prior experience in neo-RST and lower neonatal resuscitation knowledge scores than those of HCPs group. After the workshop, residents had higher overall ISSA score than that of HCPs group (90.2 ± 5.9 vs. 86.3 ± 6.6%, P = 0.003, respectively). There was no significant difference in the numbers of participants scored <80% in residents and HCPs group (3 [6.3%] vs. 7 [14.6%], respectively). Regarding the categorical scores, residents scored significantly higher in preparation, ventilation, crisis resource management and behavioral skills but lower in appropriate oxygen use, when compared with the HCPs. Conclusion Neo-RST for residents is feasible with promising short-term educational outcomes. Neo-RST could be implemented in standardized medical residency programs in China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".