Nursing students’ resilience in response to stress related to the COVID-19 pandemic
Bibliographic record
Abstract
Background and purpose: The recent consequences of the COVID 19 pandemic thrust students into unfamiliar learning environments creating an additional stressor to their personal and academic lives. The purpose of this study was to explore the perceived level of stress, resilience, and coping mechanisms of the nursing students facing the challenges associated with transitioning to a virtual instructional platform during the COVID-19 pandemic.Methods: Design: The study utilized a cross-sectional design. Using Qualtrics, electronic surveys were distributed to all nursing students in the undergraduate and graduate programs. The study measures used were: Connor Davidson-Resilience Scale and Perceived Stress Scale. Three open-ended questions were included in the survey to identify stressors and coping strategies. SPSS was used to analyze the data using descriptive statistics and correlational analyses. Thematic analysis was used to analyze the answers to the open-ended questions.Results: A total of 148 students completed the survey. Using the Perceived Stress Scale, the undergraduate nursing students had a higher mean than the graduate students (p < .05), indicating that the undergraduate students had a higher stress level. Using the Connor Davidson-Resilience Scale, the graduate nursing students had a higher resilience level than the undergraduate students (p < .005), indicating that the graduate students had more resilience than the undergraduate students. Recurrent stressors and coping strategies were identified by the participants. Conclusions: The study provides a better understanding of nursing students’ stress and resilience levels in response to significant unexpected occurrences impacting academic life. Strategies and interventions can be implemented to promote students’ resilience and improve their well-being during high-level stressful situations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".