The incidence of nursing students’ perceived stress and burnout levels at a private university in California
Bibliographic record
Abstract
Background and objective: Historically, there has been a paucity of research regarding stress and burnout in nursing students. However, during the past five years research focusing on the predictors associated with stress and burnout has been conducted. Continued research contributions to the nursing education literature are necessary due to the current 70% nursing burnout rate. The aim of this study was to explore undergraduate and graduate nursing students’ perceived stress and burnout in order to design a future intervention study.Methods: Pollock’s Nursing Adaptation Model served as a conceptual framework. Correlational descriptive non-interventional survey design was used to gather data from consented participants (N = 217). The Perceived Stress Scale and Maslach’s Burnout Inventory were provided. Data were analyzed using descriptive statistics, one-way analysis of variance, and multiple comparison t-tests (Tukey’s adjustment).Results: Students who spent more time per week on homework and studying for exams tended to be more stressed and cynical. Students enrolled in graduate level courses tended to be more cynical and exhausted. Undergraduate students demonstrated a stronger sense of professional efficacy. Students who spent less than five hours studying for exams per week reported more exhaustion, suggesting external factors may be influential. Certain recreational activities were found to be related to stress, cynicism and exhaustion levels, suggesting students with a recreational outlet may be better able to cope. Significant associations between students’ hours spent on academic work and family circumstances may provide an explanation of academic pressures.Conclusions: Our findings highlight nursing students having significantly higher stress and cynicism levels associated with the amount of homework and study hours for exams per week. Furthermore, students studying less reported being more exhausted. Collaboratively, nurse educators and students are wise to develop healthy interventions to enhance students’ health and learning. Reportedly, healthcare providers are experiencing burnout and unhealthy stress-coping behaviors. Educators are in a position to role model and educate healthy lifestyle choices.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".