The Similarities and Differences in Transition to Practice Experiences for New-Graduate RNs and Practical Nurses in Long-term Care
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to describe the transition-to-practice experience of new-graduate nurses (NGNs) in long-term-care (LTC) settings. BACKGROUND: Transitioning to professional practice is a challenging time for an NGN. This experience is scarcely described for RNs outside of acute care settings and not described for the LPN. METHODS: A qualitative case study was used to explore the described transition-to-practice experience of new-graduate RNs and LPNs in LTC. RESULTS: This study revealed that the transition-to-practice experience of new-graduate LPNs was similar to the experience described by RNs. Differences in experience were related to leadership roles in the setting. CONCLUSIONS: Findings contribute to new understanding of the experience of the NGN in LTC settings. This study reinforces the need for greater support for nursing graduates in this setting.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".