Enhancing Transsectoral Interdisciplinary Patient-Centered Care for Patients With Rare Cancers: Protocol for a Mixed Methods Process Evaluation
Bibliographic record
Abstract
BACKGROUND: Rare cancers account for approximately 24% of all new cancers. The category of rare tumor diseases includes almost 200 different entities. In particular, the treatment of patients with extensive care needs requires cooperation between service providers, both between sectors (outpatient and inpatient) and within sectors (eg, between different medical disciplines). The treatment pathway is associated with a high need for coordination and information sharing between providers. When crossing sectoral boundaries in the German health care system, interface problems between the outpatient and inpatient sectors can lead to gaps in care delivery. The multicomponent program Trans-sectoral Personalised Care Concept for Patients with Rare Cancers aims to optimize transsectoral cooperation and coordination of care to enhance patient involvement and the medical care coordination of patients with rare cancers. OBJECTIVE: This process evaluation will contribute to answering questions about intervention fidelity and the implementation of transsectoral communication, identifying and describing the intended and nonintended effects of the intervention, and exploring the barriers to and facilitators of the implementation. METHODS: We will include patients who participate in the intervention phase; all persons and staff involved in the development and implementation of the intervention (Onco Coach, psychologists, physicians on the contact platform, IT staff, and staff of the Bavarian Association of Statutory Health Insurance Physicians); physicians from the Ludwig-Maximilians-University Hospital Munich and the hospital of the Technical University Munich who are involved in the treatment of patients during the course of the project; and participating office-based hematologists and oncologists. Data collection will be conducted at the beginning, during, and at the end of the intervention using mixed methods. Data will be collected from questionnaires, document analyses, semistructured interviews, and structured observations and will cover different aspects of process evaluation. These include examining the context to explore existing patterns, changes in patterns, attitudes, and interactions; analyzing the implementation of intervention elements; and exploring the complex causal pathways and mediators of the intervention. Qualitative data will be analyzed using thematic analysis. The data will then be combined using between-methods triangulation. RESULTS: This project received funding on March 1, 2022. The intervention phase and recruitment for the process evaluation began on March 1, 2023, and the recruitment is expected to end on September 30, 2025. At the time of protocol submission in June 2023, a total of 8 doctors from hematology and oncology practices were enrolled. Data collection began on March 14, 2023. CONCLUSIONS: The Trans-sectoral Personalised Care Concept for Patients with Rare Cancers project is a complex intervention that is to be implemented in an equally complex health care context. The process evaluation will help understand the influence of contextual factors and assess the mechanisms of change. TRIAL REGISTRATION: ISRCTN registry ISRCTN16441179; https://doi.org/10.1186/ISRCTN16441179. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/49731.
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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 | 0.128 | 0.089 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.047 | 0.007 |
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".