Association between senior nursing students’ perceived stress and learning environment in clinical practice
Bibliographic record
Abstract
Background and objective: Senior nursing students encounter various stressors during their practice in clinical learning environment. Inability to manage these stressors, may affect students’ academic achievement and well-being, which in turn put the nursing profession at risk. This study aimed to examine the association between senior nursing students' perceived stress and learning environment in clinical practice and their coping strategies.Methods: Study subject consisted of 400 senior nursing students enrolled in the 4th academic year at Faculty of nursing-Tanta University. Present study used descriptive design. Four tools were used to collect the data: Nursing Students’ Perception of Stress in Clinical Practice, Physio-Psycho-Social Response Scale, Coping Strategies Scale, and Dundee Ready Education Environment Measure.Results: Senior nursing students’ experienced a high level of stress in clinical practice with total mean (2.87), and their response to stress indicated poor health status with total mean (2.76). Their learning environment need more effort to be improved with total mean score (2.4). There were high statistical significant positive correlation between students’ perception of learning environment and their responses to stress (p < .001).Conclusions: Senior nursing students experienced high level of stress in clinical practice; their responses to stress indicated a poor health status. Majority of them used problem focused disengagement and emotional focused disengagement strategies to deal with stress in clinical practice. Their perception of learning environment indicated a more positive than negative, so learning environment need more effort to be improved. Accordingly we recommend promote healthy, supportive learning environment and refine nursing curricula.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".