The Value of the Nurse Navigator in Complex Cancer Care: A Scoping Review
Bibliographic record
Abstract
Background/Objectives: Many Canadians experience challenges navigating the healthcare system during their cancer care. Nurse navigators are uniquely positioned to support patients with their clinical expertise in oncology and patient care, but they have not been widely implemented. This study aimed to examine the impact of nurse navigators and barriers to successful implementation of a nurse navigator program. Methods: MEDLINE, EMBASE, and Web of Science databases were searched for articles examining the role of nurse navigators in cancer care. The data was extracted on study design, patient characteristics, nurse navigators’ responsibilities, outcomes, barriers to success, and recommendations for implementing nurse navigator programs. Content analysis was used to identify common themes. Results: Of 1787 articles identified, 44 articles met the inclusion criteria and underwent data extraction. Nurse navigator responsibilities included patient education, psychosocial support, clinical assessment, care coordination, patient advocacy, and improving workflows. Most studies reported significant benefits from nurse navigator programs, including patient-centered care, satisfaction with the healthcare system, reduced patient distress, healthcare provider support, and enhanced patient monitoring. Barriers included a lack of understanding of the role, overwhelmed nurse navigators, and inefficient healthcare system workflows. Recommendations for future nurse navigator programs include providing personalized support to patients, encouraging integrated healthcare teams, and permanent funding. Conclusions: Nurse navigator programs improve cancer patients’ experiences and the efficiency of cancer care delivery. Implementation necessitates integration into the healthcare team and longitudinal financial and professional support of 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 | 0.015 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".