Implementating a Clinical Trial Navigator Program for Cancer Patients: Barriers & Facilitators Identified Through Stakeholder Perspectives
Bibliographic record
Abstract
Background: Patient navigation has been highlighted as a solution to improve clinical trial access. The Clinical Trial Navigator (CTN) Program is a Canadian cancer clinical trial navigation program that can be accessed online by patients or healthcare professionals (HCP). Trained individuals search and provide patients and/or oncologists a report of potentially eligible trials for free. Over 550 patients have used the Program since its launch in 2019, but systemic implementation within cancer centers has yet to occur. Objectives: We aimed to identify facilitators and barriers to implementing the CTN Program in Canadian cancer centers through stakeholder insights. Methods: We conducted 33 virtual, semi-structured interviews (45 min each) with healthcare/clinical research professionals (n=9) and patient-focused stakeholders (n=24). Interviews were guided by the Consolidated Framework for Implementation Research (CFIR) and analyzed using thematic analysis with deductive and inductive coding. Results: Participants emphasized patient navigation as a key solution to identifying and accessing clinical trials, reducing oncologist workload. Key barriers included financial and logistical stress for patients enrolling in trials at different institutions and challenges in obtaining required medical information for effective searches. When patients initiated searches, they often needed additional support discussing results with their oncologist. Conclusion: Findings highlight critical considerations for implementing the CTN Program in Canadian cancer centers. Planned program adaptations aim to address these barriers, with future evaluation on uptake and effectiveness.
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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 | 0.039 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".