Protocol for a Case Study to Explore the Transition to Practice of New Graduate Nurses in Long-Term Care
Bibliographic record
Abstract
A qualitative case study protocol for an exploration of the transition to practice of new graduate nurses in long-term care is presented. For the new graduated nurse, the transition to professional practice is neither simple nor easy. This time of transition has been examined within the hospital setting, but little work has been done from the perspective and context of long-term care. As the global population continues to age and the acuity of persons accessing services outside of hospital continues to increase, there is a need to better understand the transition experience of new graduate nurses in alternative, tertiary settings such as long-term care. Therefore, the purpose of this report is to situate a study and describe a protocol that explored the transition to practice experience of seven new graduate nurses in long-term care using Yin's case study methodology. The case or phenomenon being explored is new graduate nurse transition to practice. This report presents an overview of the literature in order to situate and describe the case under study, a thorough description of the binding of the case as well as the data sources utilized, and ultimately reflects upon the lessons learned using this methodology. The lessons learned include challenges related to precise case binding, the role and importance of context in conducting case study research, and difficulties in disseminating study findings. Overall, this report provides a detailed example of the application of the case study design through description of a study protocol in order to facilitate learning about this complex and often improperly utilized study design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.094 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.083 | 0.019 |
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".