Implementation of a clinical trial navigation program for cancer patients: Barriers and facilitators identified through stakeholder perspectives.
Bibliographic record
Abstract
1608 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. We aimed to identify facilitators and barriers to implementing the CTN Program in Canadian cancer centers by gathering insights from key stakeholders. Methods: Thirty-three 45-minute, virtual, semi-structured interviews were conducted with healthcare/clinical research professionals (CRP; n = 9) and patient-focused stakeholders (n = 24). Interviews were guided by the Consolidated Framework for Implementation Research (CFIR) and analyzed by two independent researchers using thematic analyses with deductive and inductive coding. Results: Participants highlighted the importance of patient navigation to address barriers related to the limited availability of clinical trials and difficulty in identifying them, noting that navigation can significantly reduce this workload. CRP: “ We need a program dedicated to look at trials across the board. [The clinical trial unit team] has no time or tools to be able to do this for patients.” Key barriers to implementing navigation were the financial and logistical stressors for patients who may want to enroll onto trials that the navigator finds, particularly when only available in another institution. HCP: “[Our province] covers only travel for the consultation, so [financing] is a big barrier and needs to be thought through.” Another commonly cited barrier was obtaining the required medical information for the CTN Program to perform high quality clinical trial searches. Cancer advocacy group leader: “It's got to be very physician structured because [the CTN Program intake form] needs patient records. I’ll ask patients what stage they are at and they don't know, so asking them for their medical information [to perform a clinical trial search], they just don't know that.” When the clinical trial search is initiated by patients and the report of potential eligible trials returned to them, patients felt they needed extra support in discussing the report with their oncologist. Patient: “Every oncologist is different. Some are very easy to talk to...one was extremely difficult...so to have a discussion is very difficult.” Conclusions: Our findings provide critical considerations for the successful implementation of the CTN Program in cancer centers across Canada. We have planned program adaptations to address these results and will evaluate changes in uptake and effectiveness of the CTN Program.
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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 | 0.050 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".