The impact of COVID‐19 on nursing students' lives and online learning: A cross‐sectional survey
Bibliographic record
Abstract
AIMS: To explore the impact of COVID-19 on students' lives and their online learning experience. DESIGN: A cross-sectional survey design was used in this study. METHODS: A total of 44 nursing students who were enrolled in an undergraduate programme at a Canadian University participated in the study. The students were asked to fill out a 35-item survey that was developed by the European Students' Union and that was circulated across Europe in April 2020. RESULTS: The COVID-19 pandemic and subsequent lockdown affected students mentally, and emotionally. Findings also revealed that whilst most students had the privilege to study from home, many students did not have a desk, or a quiet place to study in their home and some had problems with Internet connectivity. Online lectures were delivered according to students' preferences; however, students were dissatisfied with the way their practice was organized. CONCLUSION: The similarities between this study and the European study provide common grounds for academics around the world to connect, collaborate and work on the challenges in providing nurse education in emergencies such as national disasters or pandemics to ensure preparedness for such future events. PATIENT OR PUBLIC CONTRIBUTION: No Patient or Public Contribution. IMPACT: The commonalities experienced in nursing education across the globe should act as an impetus for globalized nursing action. Educators need to prepare and reinvent a role for students in the clinical area in the event of future disasters/pandemics. Policy makers and administrators need to ensure when switching to online education no student is underprivileged or marginalized in the process.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".