Using critical creativity to support virtual methods of critical reflection for undergraduate and graduate nursing students
Bibliographic record
Abstract
The COVID-19 pandemic has posed significant challenges to nursing education, given the ongoing changes in clinical nursing practice and the shift to virtual learning formats. This is particularly difficult to navigate for individuals who are nurses and simultaneously pursuing nursing education, at the graduate or undergraduate level. The purpose of this paper is to provide a description of a virtual approach to enhance critical reflection for individuals experiencing the pandemic as nurses and nursing students. The approach is compatible with virtual teaching methods and strongly supported by critical creativity and practice development methods. Through a series of custom-designed YouTube videos, students were asynchronously supported to create an aesthetic piece (e.g., drawing, poem, etc.) that captured their experiences during the first wave of the COVID-19 pandemic. Students also provided narratives to describe their aesthetic piece and further explicate their experiences, based on focused reflective questions. Our research team showcased the study findings in visual and written formats using an arts-based website. Sharing these methods could support nursing educators to continue supporting students in meaningful critical reflection in virtual formats.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".