Enabling Personalized Needs-Based Cancer Patient Navigation Using a Caring Life-Course Approach
Bibliographic record
Abstract
OBJECTIVES: Evidence suggests that patient navigation can help address ongoing barriers to accessing timely, appropriate, and quality cancer care. Patient navigation interventions include education, logistical, social, and emotional support, facilitating referrals, care coordination, patient advocacy, and enabling self-management. We propose that a person-centered approach to cancer patient navigation could be strengthened by the Caring Life-Course Theory (CLCT). METHODS: This discussion paper draws on relevant evidence, policy, and theory to propose a way of considering patient navigation service provision reflective of personal biographies, lived experiences, social networks, and broader structural, community, and healthcare contexts. RESULTS: A CLCT-informed, personalized, needs-based patient navigation program in cancer care would facilitate a wider range of patient-centered choices and optimize self-management and self-care by integrating biographical inquiry and care networks, thus improving the delivery and personalization of navigation services. Enhanced technology should be used to support a dynamic approach to patient navigation and develop biographically informed assessment tools and care plans that triage patients to different levels of navigation according to patient needs, self-care abilities, and capacity. CONCLUSIONS: We propose that a person-centered, needs-based approach to patient navigation can be informed by the CLCT, taking into consideration the holistic needs of people affected by cancer and developing approaches to optimize self-management and self-care in relation to these needs. IMPLICATIONS FOR NURSING PRACTICE: Cancer nurses, as holistic care providers, are well-positioned to lead the development and delivery of biographically and social network-informed navigation needs, assessments, and structured patient navigation services.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".