Bibliographic record
Abstract
AIM: This review will explore the lived experience of the transition of new nursing graduates in their first year of practice, the implications to nursing and consequences of status quo, and actions required to support new graduates in their transition to practice. BACKGROUND: The new graduate eagerly anticipates their first position in the 'real world' but often experience challenges in their first year of practice. EVALUATION: A literature review highlights the historical inaction and the confirmed lived experiences of new graduate nurses. KEY ISSUES: New graduate transition into the workforce has implications on both an individual and societal level. No longer can one ignore the need to recruit and retain nurses, especially new graduates. CONCLUSION: Implemented collaborative and innovative efforts are required to support new graduate nurse transition to practice. IMPLICATIONS FOR NURSING MANAGEMENT: Nurse Managers must question why the disenfranchisement and marginalization of new graduates continues. Persistent inertia impacts recruitment and retention of graduate nurses and patient safety, transforming episodic challenges into chronic systemic issues. This article will contribute to new nursing knowledge by providing a Canadian perspective of demographic trends of the Registered Nurse (RN) and salient actions required to resolve the discourse of new graduate transition into the workplace.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".