Nurse navigation for older adults with cancer: A scoping review
Bibliographic record
Abstract
INTRODUCTION: Nurse navigation is important in cancer care, with prior studies indicating improvements in quality of life and symptom management. We sought to understand the scope of literature on nurse navigation for older adults with cancer. Specifically, we aimed to understand the characteristics, roles, and services nurse navigators provide for older adults with cancer. MATERIALS AND METHODS: Using the updated Arksey and O'Malley scoping review method, we conducted a scoping review of the literature with searches in PubMed, CINAHL, Embase, and Cochrane. Two reviewers independently screened studies following the inclusion and exclusion criteria. We included studies: (1) published between 2000 and 2024 in English;(2) with participants with cancer aged ≥60 years or mean or median age ≥ 60 years, or a sub-group analysis of those aged ≥60; and (3) navigation provided by a nurse. We excluded grey literature and studies on nurse navigation in cancer screening. RESULTS: Our search and citation screening yielded 1291 and 748 studies, respectively, of which we screened 1528 titles/abstracts and 257 full-text articles. We identified 29 studies meeting inclusion criteria which describe nurse navigation for older adults with cancer addressing several outcomes including patient experience, satisfaction, and timeliness. About half (14/29) of the included studies addressed older adults specifically. DISCUSSION: Nurse navigation shows promise in bridging critical gaps in the care of older adults with cancer. Our findings highlight the need for further evidence about models of care and development of practice-guiding documents to enhance the effectiveness of nurse navigation for older adults with cancer.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".