Successful Components for Developing an Oncology Nurse Residency Program: An Integrative Review
Bibliographic record
Abstract
Problem Identification Residency programs for newly licensed registered nurses (NLRNs) have become an integral part of the new nurse’s transition to professional practice, improving resilience and retention of the NLRN. The goal of this integrative review is to highlight concepts and components that have been successfully incorporated into NLRN residencies and provide nursing leaders with actionable guidance to incorporate into facility-based oncology nurse residencies. Literature Search An integrative review using Whittemore and Knafl’s approach was conducted in accordance with principles of thematic analysis established by Toronto and Remington. Data Evaluation Eighteen records were included. As few research articles were found addressing the topic, a broader emerging synthesis approach was adopted to include program evaluation articles. Synthesis Five themes were identified as integral to successful NLRN residency programs: enculturation, competency validation, professional growth, preceptor support and training, and looping. Implications for Practice or Research The paucity of research regarding successful components of oncology nurse residencies offers an opportunity for future research to meet the evolving needs of NLRNs. Knowledge Translation These findings reinforce the need for nursing leaders to invest in the transition of NLRNs to professional practice. By intentionally integrating the NLRN into the organization, supporting preceptors, and validating competency, nursing leaders may improve the transition to registered professional nurse.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 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 teacher head, 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".