Abstract B001: Implementation Science-Informed Development of a Comprehensive Early Onset Cancer Program: Provider Perspectives
Bibliographic record
Abstract
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.
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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 | 0.037 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".