Enhancing Transsectoral Interdisciplinary Patient-Centered Care for Patients With Rare Cancers: Protocol for a Mixed Methods Process Evaluation
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,128 | 0,089 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,005 | 0,007 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,007 | 0,004 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,006 | 0,007 |
| Intégrité de la recherche | 0,006 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,047 | 0,007 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».