The Clinical Effectiveness of Virtual Nursing Process Education During the COVID-19 Pandemic: An Educational Evaluation and a Quasi-experimental Longitudinal Study
Bibliographic record
Abstract
Background: The COVID-19 pandemic has changed the mode of education, causing universities to shift from face-to-face to online delivery mode for offering various courses and programs. Thus, it is essential to focus on the curriculum content to tackle the challenges caused by the COVID-19 pandemic and provide methods to effectively educate nursing students in nursing practice. Objectives: This study aimed to determine the clinical effectiveness of virtual nursing process (NP) education in undergraduate nursing students during the COVID-19 pandemic. Methods: This educational evaluation study with a quasi-experimental longitudinal design was applied in four educational steps over six months for first-year undergraduate nursing students (n = 30) using Iran's national learning virtual environment. Results: The paired t-test results showed a significant difference in the mean and overall scores of nursing diagnosis and nursing outcomes/goals before and after the virtual intervention (P < 0.001). Six months after virtual education, the students registered for the ‘clinical nursing process (CNP) unit in the third semester. In the final step of the study, the overall mean score of the students' nursing care plan was 16.59 ± 2.31, which was higher than the mean score, meaning virtual NP education was effective in a clinical setting. Conclusions: Virtual mode of teaching can be effective for theoretical and CNP education.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".