Stress and exhaustion among nursing students during their final year: A cross-sectional study
Bibliographic record
Abstract
Background and objective: Nursing students face challenges in nursing education. Prior research has identified depression and fatigue as prevalent issues among nursing students, attributing these concerns to inadequate support from faculty and disorganised coursework, which contribute significantly to student exhaustion. Consequently, this study aimed to explore the relationship between stress and educational fatigue among final-semester nursing students to provide evidence that can help prevent stress and its negative health impacts, ultimately enhancing the well-being of nursing students. Methods: This cross-sectional study investigated 56 final-semester nursing students using self-administered questionnaires: the Karolinska Exhaustion Disorder Scale (KEDS) and the Higher Education Stress Inventory (HSEI). Data were analysed using descriptive statistics and bivariate analysis using Spearman correlation coefficient. Results: The study found that final-semester nursing students experienced high stress (Mean 21.84, SD = 7.065) due to workload (Mean 8.00, SD = 1.68), insufficient feedback (Mean 5.16, SD = 1.35), and faculty shortcomings (Mean 16.04, SD = 2.90). Significant positive correlations were noted between concentration and insufficient feedback (r = .29, p = .30) and low commitment (r = .27, p = .39). Physical stamina correlated significantly with workload (r = .41, p = .001) and low commitment (r = .36, p = .007). Memory and sleep showed no association with education-related stress. Conclusions: The study found that final-semester nursing students reported workload and insufficient feedback as the factors related to education-related stress. There was no link between education-related stress and variables such as memory and sleep. These findings highlight the need to address a supportive learning environment and facilitate overall student health.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".