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 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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".