Justifying oncology nurse navigator roles in Canada: an integrative literature review on quality indicators, evaluation metrics, and measurable patient outcomes
Bibliographic record
Abstract
Research has demonstrated that patient navigation interventions that have been led by nurse navigators are an effective way of improving continuity of care and the overall quality of care delivery to persons living with cancer. However, Canadian cancer programs struggle to justify the need for more nurse navigator positions. The purpose of this literature review was to explore the patient navigation movement and the known quality indicators and measurable patient outcomes available to measure the impact of implementing patient navigation initiatives that have been led by nurses. The current state of the science examining patient navigation interventions and evaluation metrics was reviewed following the integrative literature review framework. The findings are summarized in five overarching meta-themes: system efficiency, patient satisfaction, healthcare usage, return on investment, and survival. Oncology nurse navigators engender positive outcomes in oncology care. The identification of quality ambulatory oncology nursing indicators, evaluation metrics, and measurable patient outcomes may help facilitate discussions concerning the value of oncology nurse navigators and provide organizations with the tools to measure the impact of implementing nurse navigators. Patient navigation is understudied and more nursing research is needed to define oncology nursing navigator interventions and associated outcomes.
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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 | 0.138 | 0.376 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.018 | 0.034 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".