Online Tea Cafés: Using Caring Science to Transform Digital Learning Spaces and Advance Nursing Leadership
Bibliographic record
Abstract
Given the current trend toward online nursing education and the recent changes to teaching and learning modalities as a result of a global pandemic, developing a distance-learning pedagogy for students that seeks to explore the power of compassion in the digital world is both timely and necessary. Drawing on pedagogical strategies used in an online nursing course including asynchronous online discussions called Tea Cafés, the authors showcase how they advanced knowledge and understanding in relation to nursing leadership and professional formation. By underpinning the authors’ distance-learning pedagogy in caring science, students not only thrived, but created a strong sense of community, developed leadership skills, and evolved their understanding of how to leverage nursing knowledge via compassion, reflexivity, and politicization to advocate for historically underrepresented communities. By way of student feedback and performance in relation to course learning outcomes, the authors concluded that a pedagogical strategy grounded in caring science can create a reflexive, compassionate, and politicized digital space for transformative learning for both student and educator alike.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".