Barriers and Facilitators to Implementing the Clinical Trial Navigator Program: A Qualitative Analysis
Bibliographic record
Abstract
Purpose: The Clinical Trials Navigator (CTN) Program was launched in 2019 to increase enrollment in cancer clinical trials. It is free for all Canadians, and over 550 people have participated. This study has the objective of assessing the perspectives of people with cancer regarding the implementation of clinical trial navigation in Canada. Methods: People with cancer and their caregivers (n=21) were recruited from across Canada to participate in a 30-60-minute semi-structured interview. Based on the Consolidated Framework for Implementation Research (CFIR) domains, we assessed the facilitators, and barriers to access clinical trial navigation in Canada. Thematic analyses were performed by two independent researchers in duplicate using inductive and deductive coding. Results: 10 Interviewees had contacted the CTN Program (Pre-CTN) but had not yet received navigation for clinical trials, 10 had never contacted the CTN Program (Non-CTN) and 1 completed the CTN Program (Post-CTN). The results indicate participants valued early and direct access to information trials and perceived the CTN Program as a unique, trustworthy resource created by Canadians. Participants reported a sense of relief knowing they can find a trial when needed. Not all oncologists provided information for identifying clinical trials, and the CTN Program was therefore perceived as an important solution. Conclusion: Our results illustrate the gaps in the Canadian clinical trial ecosystem and emphasize the value of the CTN Program. The CTN Program addresses issues such as the timeliness and legitimacy of clinical trials information, increases patient sense of control and alleviates the burden of unexplored options.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".