“Theory Becoming Alive”: The Learning Transition Process of Newly Graduated Nurses in Canada
Bibliographic record
Abstract
BACKGROUND: Newly graduated nurses often encounter a gap between theory and practice in clinical settings. Although this has been the focus of considerable research, little is known about the learning transition process. PURPOSE: The purpose of this study was to explore the experiences of newly graduated nurses in acute healthcare settings within Canada. This study was conducted to gain a greater understanding of the experiences and challenges faced by graduates. METHODS: Grounded theory method was utilized with a sample of 14 registered nurses who were employed in acute-care settings. Data were collected using in-depth interviews. Constant comparative analysis was used to analyze data. RESULTS: Findings revealed a core category, "Theory Becoming Alive," and four supporting categories: Entry into Practice, Immersion, Committing, and Evolving. Theory Becoming Alive described the process of new graduate nurses' clinical learning experiences as well as the challenges that they encountered in clinical settings after graduating. CONCLUSIONS: This research provides a greater understanding of learning process of new graduate nurses in Canada. It highlights the importance of providing supportive environments to assist new graduate nurses to develop confidence as independent registered nurses in clinical areas. Future research directions as well as supportive educational strategies are described.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.014 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".