Impact of the COVID-19 Pandemic on Nursing Students’ Clinical Learning Experiences in British Columbia: The Student Perspective
Bibliographic record
Abstract
The clinical learning environment is where nursing students gain knowledge, practice skills and become socialized into the nursing profession. Traditionally, in Canada, the clinical learning environment takes place hospital and community settings. The COVID-19 pandemic required quick thinking by nursing faculty to replace on-site clinical hours. Given the quick change-over, there was minimal consideration given to nursing students’ perspectives in the planning stages. This study aimed to investigate nursing students’ experience in the clinical learning environment before and during the COVID-19 pandemic. The perceived quality of students’ learning experiences was assessed using the Clinical Learning Environment, Supervision and Nurse Teacher (CLES+T) Scale (Saarikoski et al., 2008) and open-ended questions to corroborate quantitative findings. Undergraduate baccalaureate nursing students from nine schools of nursing in British Columbia, Canada, completed an online validated survey about their different clinical learning experiences. Paired t-tests were conducted, revealing no statistically significant difference in nursing students’ perceived quality of their experiences before and at the height of the COVID-19 pandemic in March 2020. Quantitative and qualitative findings highlight the importance of the clinical instructor in nursing students’ clinical experiences. Our findings are valuable as nursing faculty continue to navigate opportunities for learning in the clinical learning environment as COVID-19 continues to evolve.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".