Nurses as patient navigators in cancer diagnosis: review, consultation and model design
Bibliographic record
Abstract
The diagnostic phase of cancer care is an anxious time for patients. Patient navigation is a way of assisting and supporting individuals during this time. The aim of this review is to explore patient navigation and its role in the diagnostic phase of cancer care. We reviewed the literature for definitions and models of navigation, preparation for the role and impact on patient outcomes, specifically addressing the role of the nurse in patient navigation. Interviews and focus groups with healthcare providers and managers provided further insight from these stakeholder groups. Common to most definitions of navigation is the navigator's multifaceted role in facilitating processes of care, assisting patients to overcome barriers and providing information and support. Navigation may be provided by laypersons, clerical staff and/or healthcare professionals. In the diagnostic phase it has the potential to affect efficiency of diagnostic testing, patients' experience during this time and preparation for decision-making around treatment options. Patient care during the diagnostic phase requires various levels of navigation, according to individual informational, physical and psychosocial needs. Identifying those individuals who require more support--whether physical or psychosocial--during the diagnostic phase is of critical importance.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".