Nursing Students’ Perception toward Shifting to Online Learning during the COVID-19 Pandemic at Urgent and Planned Situations
Bibliographic record
Abstract
Background: The coronavirus disease 2019 (COVID-19) pandemic has negatively affected the learning strategies of nursing faculties, causing the temporary lockdown of universities and sudden shift of traditional learning to online learning without prior preparation. As lockdown continues, preparation of educational process for online learning takes place. Aim: We aimed to compare nursing students’ perception toward online learning during the COVID-19 pandemic at urgent and planned situations. Research design: A descriptive study was conducted. Setting: Data were collected at the Faculty of Nursing of Kafrelsheikh University in Egypt. Study subjects: By stratified randomization, we recruited 1004 students who already shifted to online learning during the pandemic and divided them into the urgent and planned situation groups (502 per group). Tools: Two tools were utilized: students’ technical-related data and students’ perception toward online learning, benefits, and possible challenges. Results: A statistically significant difference in nursing students’ perception toward online learning (χ2 = 356.215*, p < 0.001*) between the periods of urgent and planned situations, approximately one-quarter (20.7%) of the urgent group compared with the majority (80.3%) in the planned group had a positive overall perception. Conclusion: Nursing students’ perception during the period of planned online learning was better than that during the period of urgent online learning. Recommendations: We recommend introducing the latest innovations of online learning for both theoretical and practical lectures, including 3D virtual learning environment for practical lectures.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".