From College to University: Nursing Students' Experience of Transition
Bibliographic record
Abstract
Increasingly complex healthcare systems require nurses to have a greater depth of knowledge and theory application to care for patients safely and competently. To prepare nurses for rapidly changing health care systems, the baccalaureate degree was accepted by the Canadian Nurses Association (CNA) as the standard for entry to Registered Nurse practice across Canada and became the entry-to-practice requirement starting in 2005 (Wood, 2011).\nIn Ontario, this entry-to-practice standard required the development of college and university collaborative partnerships to respond to the resulting implementation and capacity challenges in nursing education (MacMillan & Mallette, 2004). Although these partnerships have been in place for some time, little can be found in the literature regarding issues and challenges of collaboration over time, particularly for nursing students (Cameron, 2003; Cameron, 2005; Landeen et al, 2017, Montague, et al., 2022, Molzahn & Purkis, 2004; Zorzi et al., 2007).\nThe purpose of this study is to add to what is known about transitions in nursing education for students by using case study methodology to explore how students experience the transition from the college to the university in a hybrid collaborative baccalaureate program. Understanding the transfer experience of collaborative program nursing students can enable faculty to develop strategies that ease the transition and facilitate student success in the upper years of the program. By uncovering the experience of students, nursing academicians can deepen their understanding of the complexity of student transition in entry-level collaborative nursing education, enabling student success, program completion, and transition to graduate nurse.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".