The lived experience of nursing students caring for COVID-19 patients
Bibliographic record
Abstract
Background and objective: As the wave of COVID-19 pandemic hit the world, schools and students were affected in many ways. Schools had to migrate courses to an online or hybrid platform while students had to adapt their learning to take care of COVID-19 patients in the clinical setting. Caring for COVID-19 patients in the hospital setting provided the students with big challenges, and it became essential for faculty members to understand the students’ feelings and obstacles as the semester continued.Methods: Utilizing a phenomenological framework, a qualitative descriptive study was performed to determine the lived experience of student nurses caring for COVID-19 patients.Results: Four main themes emerged from the study, which included 1) Importance of a support system, 2) Moral distress, 3) Enhancement of clinical skills, and 4) Significance of therapeutic communication.Conclusions: Based on the themes, four recommendations were identified to help students and faculty, which included 1) The value of simulation, 2) Development of a support system, 3) Collaborative preceptorship, and 4) Preparation for a new era.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".