Navigation as a system approach: A qualitative descriptive study to inform a statewide cancer navigation approach in Australia
Bibliographic record
Abstract
PURPOSE: This study aimed to identify challenges and facilitators in accessing cancer care in South Australia, from the perspectives of cancer survivors and caregivers, to inform responsive cancer navigation approaches. METHODS: A qualitative descriptive study was conducted using an online qualitative survey (n = 75) and video, phone, and in-person semi-structured interviews (n = 22) with cancer survivors and caregivers (herein cancer consumers). Data analysis was performed in two phases: content analysis categorised consumer challenges and facilitators, while a subjective-inductive approach guided by the supportive care framework was used to develop a statewide navigation approach. RESULTS: Key challenges reported by consumers included perceived invalidation of medical concerns, delayed diagnoses, poor communication, inadequate information provision, fragmented care, and limited logistical, cultural, and psychological support. Inductive analysis identified four key themes: 1) cancer consumers have dynamic care needs that can evolve throughout a patient's cancer experience, 2) cancer consumers require a foundational level of information to support navigation, 3) some cancer consumers express a preference for community-based navigation services to help them manage their care, and 4) individuals with more complex care needs may require more intensive professional navigation services. A conceptual needs-based navigation approach (the Flinders Needs-Based Approach to Cancer Navigation) was developed based on these insights. This approach consists of three levels of navigation interventions: level 1 involves providing information-based navigation to all individuals affected by cancer, level 2 involves community-based navigation support offered to those requiring or wanting additional supported assistance, and level 3 offers professional navigation for individuals with complex needs. CONCLUSION: Our study highlights the importance of tailoring cancer navigation services to meet the evolving needs of patients, emphasising the role of both community and professional support, particularly for individuals with complex care requirements. Findings will inform further co-design discussions involving consumers, health professionals, and policymakers to implement cancer navigation services across South Australia.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".