Developing a framework for the ethical design and conduct of pragmatic trials in healthcare: a mixed methods research protocol
Bibliographic record
Abstract
BACKGROUND: There is a widely recognized need for more pragmatic trials that evaluate interventions in real-world settings to inform decision-making by patients, providers, and health system leaders. Increasing availability of electronic health records, centralized research ethics review, and novel trial designs, combined with support and resources from governments worldwide for patient-centered research, have created an unprecedented opportunity to advance the conduct of pragmatic trials, which can ultimately improve patient health and health system outcomes. Such trials raise ethical issues that have not yet been fully addressed, with existing literature concentrating on regulations in specific jurisdictions rather than arguments grounded in ethical principles. Proposed solutions (e.g. using different regulations in "learning healthcare systems") are speculative with no guarantee of improvement over existing oversight procedures. Most importantly, the literature does not reflect a broad vision of protecting the core liberty and welfare interests of research participants. Novel ethical guidance is required. We have assembled a team of ethicists, trialists, methodologists, social scientists, knowledge users, and community members with the goal of developing guidance for the ethical design and conduct of pragmatic trials. METHODS: Our project will combine empirical and conceptual work and a consensus development process. Empirical work will: (1) identify a comprehensive list of ethical issues through interviews with a small group of key informants (e.g. trialists, ethicists, chairs of research ethics committees); (2) document current practices by reviewing a random sample of pragmatic trials and surveying authors; (3) elicit views of chairs of research ethics committees through surveys in Canada, UK, USA, France, and Australia; and (4) elicit views and experiences of community members and health system leaders through focus groups and surveys. Conceptual work will consist of an ethical analysis of identified issues and the development of new ethical solutions, outlining principles, policy options, and rationales. The consensus development process will involve an independent expert panel to develop a final guidance document. DISCUSSION: Planned output includes manuscripts, educational materials, and tailored guidance documents to inform and support researchers, research ethics committees, journal editors, regulators, and funders in the ethical design and conduct of pragmatic trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.710 | 0.934 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".