An exploration of nursing students’ stress, coping, and quality of life
Bibliographic record
Abstract
Objective: The purpose of this phenomenological study was to identify stressors nursing students experience and examine the impact of stress on quality of life. A secondary aim was to identify coping mechanisms nursing students use to manage stress and to explore the effectiveness of these coping strategies.Methods: Purposive and snowball sampling were used to recruit eight students from a baccalaureate nursing program in the southeastern United States. One on one interviews were conducted using a semi-structured interview guide. Data were analyzed using Colaizzi’s method of data analysis.Results: Three themes emerged and were identified as (a) the Culture of Nursing School, (b) Nursing School Impacts Quality of Life, and (c) Emotion-Focused Coping. The sub-themes identified with Nursing School Impacts Quality of Life included Personal Lives Altered and Fear of Failure. Within Emotion-Focused Coping emerged two subthemes, Misery Loves Company and Light at the End of the Tunnel.Conclusions: The present study sheds light on the stressors nursing students experience Many students feel unprepared and become occupied with the fear of failure, which interferes with concentration and academic performance. Stressful situations in nursing school are unavoidable, therefore students must develop effective coping strategies. Faculty need to incorporate coping education into the curriculum to produce resilient, successful nurses. Fostering adaptive coping strategies may help decrease burnout and improve physical and mental wellbeing in 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".