Abstract B001: Implementation Science-Informed Development of a Comprehensive Early Onset Cancer Program: Provider Perspectives
Notice bibliographique
Résumé
Abstract Introduction: Patients with Early Onset Cancer (EOC, defined as cancer diagnosed in individuals ages 18-49), have distinct clinical and psychosocial needs that require dedicated strategies. Therefore, we utilized the Consolidated Framework for Implementation Research (CFIR) to assess the needs of healthcare providers (physicians and clinical staff) to inform the development of an EOC program. Methods: The needs assessment included eleven 5-point Likert-style and open-ended questions addressing the five CFIR domains (Intervention Characteristics, Outer Setting, Inner Setting, Characteristics of Individuals, and Process), adapted from validated tools. The survey was disseminated electronically to providers across subspecialties and clinical services at our institution from March to May 2024. Data collection was anonymous; analytic dataset focused on completed surveys. Descriptive statistics and thematic analysis were used to characterize results. Results: Overall, 617 providers were sent the needs assessment survey; 103 completed the entire survey (16.7%). Respondents were physicians (44.7%), nurses (24.3%), advanced practice providers (13.6%), social workers (4.9%), and others (12.5%). Specialties encompassed Medical Oncology (44.7%), Hematology (12.6%), Surgery (21.4%), Radiation Oncology (4.9%), and others (16.4%). Regarding the inner setting, most respondents (90.3%) felt that EOC was relevant to their clinical practice, but only 40.8% felt they knew how to overcome barriers to care. Many respondents (77.7%) felt aware of EOC patient needs, yet only 37.9% felt they had access to services that supported these needs. Respondents were concerned with the timeliness and availability of services for psycho-oncology, legal aid, and childcare (mean and SD, respectively, 2.16 ± 1.38, 1.84 ± 1.32, and 1.20 ± 1.19). Outer setting responses indicated that our institution is a source of care for patients with EOC (65.1%), and many agreed that comprehensive care for EOC patients would help address disparities (89.3%). Three themes emerged for improvement opportunities for patients with EOC: 1) resource development, dissemination, and awareness, 2) education for patients and providers, and 3) investigation to support advancement in this field. These themes have informed the development of an EOC program, which focuses on the diverse needs of patients with EOC and their families. Informed by this data, we developed the Coordinated Outreach to address Needs and Navigation for Early onset Cancer Together (CONNECT) Initiative, which provides targeted outreach and personalized navigation to patients with EOC. CONNECT staff work closely with clinical teams to ensure key needs and barriers to engagement in care are proactively and comprehensively addressed. Conclusion: Our needs assessment among providers revealed key insights to develop a core initiative in an EOC program. Implementation and process metrics collected through CONNECT will help inform national EO programs and support the needs of the growing early-onset cancer population. Citation Format: Thejal Srikumar, Nancy Borstelmann, Jamie Reedy, Laura Gross, Amy Leader, Veda N. Giri. Implementation Science-Informed Development of a Comprehensive Early Onset Cancer Program: Provider Perspectives [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr B001.
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 | 0,037 | 0,058 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,001 |
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 ».