Results from a Theory-Guided Survey to Support Breast Cancer Trial Participation: Barriers, Enablers, and What to Do about them
Bibliographic record
Abstract
Background: Ensuring adequate, informed, and timely participation in clinical trials is a multifactorial problem. We have previously developed a systematic, tailorable survey development approach that is informed by theory, can identify barriers and enablers to participation, and can suggest recruitment strategies to address these issues. In this study, we surveyed subscribers to the Canadian Breast Cancer Network (CBCN) in order to identify a comprehensive list of theory-informed barriers and enablers relevant to participation in a hypothetical breast cancer trial. Methods: We developed and conducted an online survey of breast cancer patients informed by the Theoretical Domains Framework and designed to determine previous experience with clinical trials, knowledge about clinical trials, and importance of a comprehensive list of barriers and enablers to trial participation. Participants were contacted by email or through social media. Results: From 2451 subscribers of the CBCN, we received 244 responses and 210 completed surveys (244/2451 or 9.9% participation, 210/244 or 86.1% completion). A total of 38% of respondents indicated experience in trial participation, but 83% indicated confidence in their knowledge about clinical trials. Those who had previously participated in clinical trials were more confident in their knowledge (χ2= 6.77, p = 0.009) and answered more knowledge questions (t = −3.90 p = 0.000). Endorsed barriers and enablers to participation included 39 factors across 12 of 14 domains relevant to behaviour change. Our approach identifies barriers that might be meaningfully addressed by careful knowledge provision (‘If I would learn more about my condition’; ‘If I find the trial documents hard to understand’), those that may require other theory-informed approaches to address (‘my feelings about the quality of my drug plan’; ‘my worry over unknown side effects’), and those that may require tailored approaches depending on participant differences such as previous experience in trials (‘If there were patient-friendly decision-making tools to help you make your participation decision’). Discussion: This work demonstrates that a comprehensive, theory-guided survey of barriers and enablers to participation in breast cancer clinical trials is feasible, can lead to detailed knowledge about the issues related to participation in specific trials, and most importantly, can lead to insights about evidence-based ways to better support patient participation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.055 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".