Changing patient preferences toward better trial recruitment: an ethical analysis
Bibliographic record
Abstract
While randomized controlled trials are essential to health research, many of these trials fail to recruit enough participants. Approaching recruitment through the lens of behavioral science can help trialists to understand influences on the decision to participate and use them to increase recruitment. Although this approach is promising, the use of behavioral influences during recruitment is in tension with the ethical principle of respect for persons, as at least some of these influences could be used to manipulate potential participants. In this paper, we examine this tension by discussing two types of behavioral influences: one example involves physician recommendations, and the other involves framing of information to exploit cognitive biases. We argue that despite the apparent tension with ethical principles, influencing trial participants through behavior change strategies can be ethically acceptable. However, we argue that trialists have a positive obligation to analyze their recruitment strategies for behavioral influences and disclose these upfront to the research ethics committee. But we also acknowledge that since neither trialists nor ethics committees are presently well equipped to perform these analyses, additional resources and guidance are needed. We close by outlining a path toward the development of such guidance.
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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 | 0.045 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.008 | 0.020 |
| 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; 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".