Patient and Public Perceptions in Canada about Decentralized and Hybrid Clinical Trials: “It’s about time we bring trials to people”
Bibliographic record
Abstract
Abstract Background Little is known about patient and the public perspectives on decentralized and hybrid clinical trials in Canada. Methods We conducted an online survey (English and French) promoted on social media to understand perspectives of people in Canada about decentralized and hybrid clinical trials. The survey had two sections. We co-produced this project entirely with patient, caregiver, and family partners. Results The survey had 284 (14 French) individuals who started or completed section 1, and 180 (16 French) individuals who started or completed section 2. People prefer to have options to participate in clinical trials where aspects are decentralized or hybridized. Seventy-nine percent of respondents preferred to have options related to study visits. There were concerns about handling adverse events or potential complications in decentralized trials, however, communication options such as a dedicated contact person for participants was deemed helpful. Most respondents were amenable to informed consent being done at a satellite site closer to home or via technology and were split on privacy concerns about this. Most preferred travel to a site within an hour, depending on what the trial was for or its impact on quality of life. Due to the response rate, we were unable to explore associations with gender, age, health status, geography, ethnicity, and prior clinical trial participation. Conclusion Our findings indicate an openness in Canada to participating in trials that decentralize or hybridize some aspects. These trials are perceived to provide benefits to participants and ways to increase equity and accessibility for participants.
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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 | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".