The perceived workload of first-line healthcare professionals during neonatal resuscitation
Why this work is in the frame
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Bibliographic record
Abstract
Background: Neonatal resuscitation is stressful for healthcare professionals as measured using the National Aeronautics and Space Administration-Task Load Index (NASA-TLX). Little is known regarding the perceived workload and associated factors among healthcare professionals including medical doctors (MDs) and nurses/midwives who have differences in training and experiences. We aimed to characterize and compare the perceived workload between MDs and nurses/midwives who provided neonatal resuscitation. Methods: In a prospectively designed, cellphone-based surveillance, perceived workload and stress of MDs and nurses/midwives during neonatal resuscitation was evaluated using a modified multi-dimensional NASA-TLX survey in three tertiary Neonatal Intensive Care Units in China. The NASA-TLX data on mental, physical, temporal demand, performance, effort, and frustration were independently rated by participants and collated to a composite score of all dimensions. Demographics of participants and deliveries were also collected for statistical analyses using univariate comparison and multiple linear regression. Results: From 410 valid surveys (187 (46%) MDs; 223 (54%) nurses/midwives), significant differences were noted between MDs and nurses/midwives including working years and dimensional and overall NASA-TLX scores. While MDs had lower overall NASA-TLX scores than nurses, their scores were inversely related with simulation-based training. More team members presence during resuscitation was associated with higher NASA-TLX scores. Other independent factors associated with NASA-TLX scores included gestational age, Apgar score at 1 min, year of practice for MDs and all resuscitation questions asked by nurses/midwives. Conclusions: MDs and nurses/midwives attending deliveries had different perceptions in workload and stress which could be lowered from simulation-based training in neonatal resuscitation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it