A Transformative Learning Experience for Senior Nursing Students
Bibliographic record
Abstract
BACKGROUND: Research suggests that clinical practicums in hospital-based settings are important, even if condensed, to provide students with the opportunity for real-world learning experiences. Rational dialogue makes learning meaningful and empowers students to learn by reflecting on experiences. PROBLEM: The COVID-19 pandemic minimized availability of traditional one-to-one mentorship practicums. APPROACH: This article describes the use of critical reflection on experiences in an undergraduate senior mentorship course to assess student learning through the thematic analysis of writing assignments. Guided by Mezirow's transformative learning theory, students completed a traditional group clinical practice, written reflective journals and virtual seminars focused on role development, and reflection on concurrent learning in clinical and simulation experiences. OUTCOMES: Transformative learning was evident in their writing. Student journals demonstrated themes of responding to change, discovering resilience, developing confidence, finding gratitude, embracing advocacy, and transforming and becoming. CONCLUSIONS: Through critical reflection, students recognized the opportunities mentorship afforded them, despite challenges.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".