Growth and Adaptation of Newly Graduated Nurses Based on Duchscher’s Stages of Transition Theory and Transition Shock Model: A Longitudinal Quantitative Study
Bibliographic record
Abstract
Aim: To examine the professional role development of new graduate nurses (NGNs) across three transition stages within a major Australian health service. Background: The shift from student to registered nurse is a critical period marked by emotional strain, role ambiguity, and transition shock. Structured support programs have emerged to ease this transition, yet few are evaluated through longitudinal, theory-informed approaches using validated tools. Design: A longitudinal quantitative study guided by Duchscher’s Stages of Transition Theory and the Transition Shock Model. Methods: A customised 75-item questionnaire, adapted from the Professional Role Transition Risk Assessment Instrument and Professional and Graduate Capability Framework, was distributed across three transition points (March 2020 to March 2021). The survey assessed four domains: Responsibilities, Role Orientation, Relationships, and Knowledge and Confidence. Descriptive statistics, Principal Component Analysis, Chi-square tests, and multinomial logistic regression were used to analyse patterns, trends, and predictors of successful transition. Results: A clear developmental trajectory emerged. Early-stage participants experienced stress, low self-efficacy, and role confusion. By Stage 3, most demonstrated increased clinical confidence, autonomy, and integration into professional healthcare teams. A coordinator-to-graduate ratio of 1:12 facilitated personalised mentorship and supportive practice environments. Statistically significant improvements were observed in critical thinking, leadership perception, emotional resilience, and team communication. Conclusions: This study provides robust empirical support for theoretically grounded and policy-aligned Graduate Nurse Transition Programs. Targeted interventions tailored to each transition phase—such as early mentorship, mid-stage stress support, and end-stage leadership development—can enhance confidence, improve role clarity, and promote long-term workforce capability and sustainability.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".