The impact of COVID-19 on nursing education in Sri Lanka: A reflective analysis
Bibliographic record
Abstract
In the wake of COVID-19, nursing education has shifted to eLearning from traditional methods. This reflective analysis addresses the impact of COVID-19 on the development of competencies among nursing students in Sri Lanka. Four themes were identified: the direct impact of the COVID-19 pandemic on nursing education; the role of eLearning in nursing education during the COVID-19 pandemic; the acquisition of nursing skills and competencies; and implications for education, research, and policy. Virtual online learning has replaced traditional teaching and learning. Acquiring clinical skills and competencies and completing the practicum are challenging. Since nurses are in high demand; nursing education needs to be accelerated and modified. A change in policies related to education and research is essential for developing countries. Blended learning, which includes more simulation teaching, is recommended.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".