Stress and cortisol as predictors of fatigue in medical/surgical nurses and nurse leaders: A biobehavioral approach
Bibliographic record
Abstract
Objective: High acuity and long work hours are significant contributors to nurses’ stress. Studies evaluating consecutive workdays with the use of biobehavioral methods are limited in the US. The aim of this study was to assess changes in and the relationship between stress, fatigue, and cortisol.Methods: In an observational within-subject design, we studied stress, fatigue and cortisol before and after 2 consecutive 12-hour day shifts in an acute care setting. Specifically, the study was designed to: (1) assess the effect of stress on fatigue; (2) examine the effect of stress on cortisol; (3) compare the levels of stress, fatigue, and cortisol; and (4) compare the responses of stress, fatigue, and cortisol between acute care, day shift staff nurses and nurse leaders.Results: Stress, fatigue, and cortisol increased significantly from baseline to Day 2 (p = .001, .004, and .010, respectively; paired t-test). In a comparison of nurses and nurse leaders, stress and fatigue at baseline were significantly higher in acute care nurses than in nurse leaders (p ≥ .00 and .05, respectively; independent t-test). At the end of 2 consecutive shifts, cortisol was significantly higher in staff nurses than in nurse leaders (p = .001).Conclusions: Competing initiatives pressure nurse leaders to work long hours to support organizational goals, sometimes at the expense of a healthy work environment. Nurses from direct care staff to executives should be educated in and demonstrate best practices in relation to endorsements from the American Nurses Association on fatigue and interventions to lessen the risks to patient safety.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".