The perceived workload of first-line healthcare professionals during neonatal resuscitation
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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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 | 0.001 | 0.006 |
| 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".