Recommendations for online learning challenges in nursing education during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: Nursing education institutions have had to change from face-to-face to online learning because of the coronavirus disease 2019 (COVID-19) pandemic. The online learning mode, however, had several challenges. OBJECTIVES: To explore and describe recommendations made to address the online learning challenges in nursing education during the COVID-19 pandemic. METHOD: This study adopted a narrative literature review to achieve its objectives. The search for the relevant literature used Google Scholar, ScienceDirect, African Journal (previously SAePublications), EBSCOhost, EBSCO Discovery Service and Scopus databases. RESULTS: There were four findings identified from the literature search: provision of adequate resources, monitoring of academic dishonesty, provision of technical support and revision of the curriculum. CONCLUSION: More work in nursing education is necessary to address the challenges of adopting online learning during and after the COVID-19 pandemic. To meet the issues of online learning in nursing education, thorough preparations and safeguards are necessary.Contribution: The outcomes of this study will benefit nursing education by incorporating recommendations from many studies to overcome online learning issues in nursing education during the COVID-19 pandemic.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".