Nurses as patient navigators in cancer diagnosis: review, consultation and model design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it