A Patient-Centered, Theory Guided Approach to Examining the Barriers and Enablers to Trial Participation amongst People with Sickle Cell Disease
Bibliographic record
Abstract
Objective: Recruitment to clinical trials involving sickle cell disease (SCD) patients can be challenging, leaving trialists uncertain about how to optimize recruitment approaches and strategies. Informed by the Theoretical Domains Framework (TDF), we identified a comprehensive set of barriers and enablers to participation in SCD trials, and suggest how this theory-informed survey approach can improve trial recruitment strategies. Methods: In collaboration with Clinical Trials Ontario and Sickle Cell Awareness Group of Ontario (SCAGO), we conducted a mixed methods study involving interviews with and surveys of SCD patients and families. We iteratively adapted a template survey based on think-aloud interviews, before administering the adapted survey online to SCAGO membership. Results: Fifteen interviews with SCAGO members led to 49 survey items across 13 of 14 TDF domains. Four new items specific to the SCD community were added. Administration challenges led to low survey response, with only 22 people completing the survey. Eighteen items from 8 domains were seen as barriers (eg invasive tests/procedures, travel to study site). Twenty-two items from 9 domains were seen as enablers (eg hope for a cure, helping others). Conclusion: Our theory-guided approach identified a comprehensive set of factors related to SCD trial participation, information that can support recruitment strategy development prior to trial onset. Low survey response rates precluded strong conclusions about the relative priority of the individual barriers and enablers; more work will be needed among a broader sample of SCD patients and families. Identification of theory-guided behavioral domains offers targeted suggestions for trial recruitment.
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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 | 0.105 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".