Exploring the Intersection Between Academic and Professional Practice During the COVID-19 Pandemic: Undergraduate and Graduate Nursing Students’ Experiences
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease-2019 (COVID-19) pandemic has implications for students who are also nurses. PURPOSE AND METHODS: This qualitative descriptive study used a practice development approach to explore the intersection between academic and professional work experiences for undergraduate Post-Diploma Registered Practical Nurses bridging to Registered Nurse Bachelor of Science in Nursing students and Master of Nursing graduate nursing students during the first wave of the COVID-19 pandemic. The study incorporated critical aesthetic reflections that focused on the personal and aesthetic ways of knowing, as a data collection approach and knowledge dissemination strategy. RESULTS: Analysis of the narrative component of participants' reflections revealed the following themes: sensing a "call to duty," experiencing a myriad of emotions, shifting societal and individual perceptions of nursing, and learning in an uncertain environment. CONCLUSIONS: The results of the study can inform educational strategies and academic policies to support this unique nursing population, who are frontline practitioners as well as student learners.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.018 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| 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".