Examining Shift Length and Fatigue
Bibliographic record
Abstract
BACKGROUND: Neonatal advanced practice providers (APPs) often work prolonged hours in high-acuity neonatal intensive care units (NICUs). It is imperative to understand how fatigue affects the APP's ability to react quickly following long shifts. There is a lack of data on the effects of shift length and fatigue on neonatal APP job performance and clinical decision-making. PURPOSE: The purpose of this study was to describe the variation in shift length, knowledge-based competency, personal well-being, and behavioral alertness for neonatal APPs. METHODS: This study evaluated neonatal APPs before and after a clinical shift. Provider well-being was assessed during the pre-survey. Pretest-posttest surveys evaluated neonatal APP's psychomotor vigilance skills and knowledge. Participants completed an online, anonymous questionnaire to answer a series of knowledge-based questions before and after their shift, along with a psychomotor vigilance test (PVT). A paired t test analysis evaluated the pre- and post-shift PVT values and knowledge-based test scores. RESULTS: Overall, 61 pre-surveys and 42 post-surveys were completed; 36 were matched by participants pre- to post-survey. The mean between pre- and post-knowledge-based questions was statistically significant, with higher posttest scores. There was no statistical difference noted in the paired t test analysis of the PVT values. IMPLICATIONS FOR PRACTICE AND RESEARCH: The small sample size may limit the generalizability of findings, but these results may indicate that shift length does not affect psychomotor vigilance or knowledge-based competency. It is vital that future work assess the associations between APP shift length, fatigue, and critical decision-making.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 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".