EU NAVIGATE: Developing a European navigation intervention for older people with cancer and their family caregivers
Bibliographic record
Abstract
Background: Most people who have cancer are older people, and this affects millions of Europeans yearly. Integrating high-quality, equitable, and cost-effective care across the continuum of supportive, palliative, end-of-life, and survivorship care for both patients and family caregivers is highly relevant from a healthcare, prevention, and economic perspective. Aims: EU NAVIGATE is an interdisciplinary, cross-country, and intersectoral project funded by the European Union and aiming to evaluate the effectiveness and cost-effectiveness of a patient and family navigation intervention (NavCare-EU) for older people with cancer and their family caregivers in different healthcare systems in Europe. It focuses on reaching underserved individuals, supporting and empowering them to access their own networks and available resources within their community. As the first step, the existing and promising Canadian Nav-Care intervention from will be adapted, contextualized, standardized and pilot-tested across six European countries. Concept and methods and anticipated results: NavCare-EU is a person- and family-centered non-pharmacological intervention in which volunteer navigators collaborate with patients and families to facilitate timely and equitable access to health and social care services and resources as needed. The Nav-CARE intervention involves a navigation model, including international blended training, and various tools to support navigators. We will share the model, different roles involved and training packages. Following the ADAPT guidance, the Nav-Care intervention will be translated and adapted in close collaboration with Local Adaptation Teams diverse users, and other stakeholders (according to principles of Patient and Public Involvement). To contextualize the original intervention, we follow the extended Normalization Process Theory. An International Oversight Group will safeguard the original intervention’s integrity and key components using the Model for Adaptation Design and Impact (MADI) to guide decision-making around the design of adaptations. The feasibility of the Nav-CARE intervention within local settings will be reviewed across countries in a pilot study to understand the practicalities of implementation, maximize feasibility, and troubleshoot issues that arise during implementation. Effectiveness and cost-effectiveness will be evaluated through an international pragmatic randomized controlled trial. Impact: The EU NAVIGATE will shift the state of the art of cancer care in Europe by providing an innovative solution to the complex care needs experienced by older cancer patients and their families. we pay particular attention to local healthcare contexts to ensure optimal implementation. We will present the final program ready for testing at the conference.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".