Starting Out: qualitative perspectives of new graduate nurses and nurse leaders on transition to practice
Bibliographic record
Abstract
AIM: To describe new graduate nurses' transition experiences in Canadian healthcare settings by exploring the perspectives of new graduate nurses and nurse leaders in unit level roles. BACKGROUND: Supporting successful transition to practice is key to retaining new graduate nurses in the workforce and meeting future demand for healthcare services. METHOD: A descriptive qualitative study using inductive content analysis of focus group and interview data from 42 new graduate nurses and 28 nurse leaders from seven Canadian provinces. RESULTS: New graduate nurses and nurse leaders identified similar factors that facilitate the transition to practice including formal orientation programmes, unit cultures that encourage constructive feedback and supportive mentors. Impediments including unanticipated changes to orientation length, inadequate staffing, uncivil unit cultures and heavy workloads. CONCLUSIONS: The results show that new graduate nurses need access to transition support and resources and that nurse leaders often face organisational constraints in being able to support new graduate nurses. IMPLICATIONS FOR NURSING MANAGEMENT: Organisations should ensure that nurse leaders have the resources they need to support the positive transition of new graduate nurses including adequate staffing and realistic workloads for both experienced and new nurses. Nurse leaders should work to create unit cultures that foster learning by encouraging new graduate nurses to ask questions and seek feedback without fear of criticism or incivility.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".