The Role of the Nurse Navigator in Breast Cancer Care: Barriers and Recommendations for Implementation - A Scoping Review Protocol
Bibliographic record
Abstract
Navigating the healthcare system can be a daunting and confusing experience. Nurse navigators aim to provide cancer patients with guidance and emotional support throughout their entire journey, from screening to remission. Nurse navigator programs have been shown to increase patient satisfaction and to reduce inequalities in access to care. However, costs and resource availability may restrict implementation of this program. This scoping review aims to understand the benefits of nurse navigators, as well as the barriers to implementation. This review will outline the factors that make a successful nurse navigator program, evaluate barriers, and provide recommendations for implementing a coordinated care model with nurse navigators.
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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 | 0.124 | 0.138 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".