Association between stress and coping strategies in Chinese nursing students: A cross-sectional study
Bibliographic record
Abstract
Background and objective: Stress and mental health concerns have increasingly been studied among Chinese nursing students. Understanding stress levels in this population is essential for addressing their psychological well-being. This study aims to examine the stress levels of Chinese nursing students and explore their association with coping strategies.Methods: This cross-sectional study surveyed 180 Chinese nursing students from a medical university in Anhui during the 2022/23 academic year. Data were collected using an online self-report questionnaire assessing demographic details, stress levels (Student Nurse Stress Index), and coping strategies (Brief COPE Inventory). Descriptive statistics, correlation analysis, and stepwise regression were used for data analysis.Results: A total of 170 nursing students completed the survey, revealing a mean stress level of 52.99. Regression analysis indicated that denial, self-blame, and acceptance significantly predicted stress, with acceptance associated with lower stress and denial and self-blame linked to higher stress.Conclusions: This study highlights the impact of cultural factors on stress responses and emphasizes the potential benefits of promoting acceptance as a coping mechanism among Chinese nursing students.
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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 | 0.001 |
| 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 | 0.000 | 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".