Recommendations for online learning challenges in nursing education during the COVID-19 pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it