Thinking clearly about the FIRST trial: addressing ethical challenges in cluster randomised trials of policy interventions involving health providers
Bibliographic record
Abstract
The ethics of the Flexibility In duty hour Requirements for Surgical Trainees (FIRST) trial have been vehemently debated. Views on the ethics of the FIRST trial range from it being completely unethical to wholly unproblematic. The FIRST trial illustrates the complex ethical challenges posed by cluster randomised trials (CRTs) of policy interventions involving healthcare professionals. In what follows, we have three objectives. First, we critically review the FIRST trial controversy, finding that commentators have failed to sufficiently identify and address many of the relevant ethical issues. The 2012 Ottawa Statement on the Ethical Design and Conduct of Cluster Randomized Trials provides researchers and research ethics committees with specific guidance for the ethical design and conduct of CRTs. Second, we aim to demonstrate how the Ottawa Statement provides much-needed clarity to the ethical issues in the FIRST trial, including: research participant identification; consent requirements; gatekeeper roles; benefit-harm analysis and identification of vulnerable participants. We nonetheless also find that the FIRST trial raises ethical issues not adequately addressed by the Ottawa Statement. Hence, third and finally, we raise important questions requiring further ethical analysis and guidance, including: Does clinical equipoise apply to policy interventions with little or no evidence-base? Do healthcare providers have an obligation to participate in research? Does the power-differential in certain healthcare settings render healthcare providers vulnerable to duress and coercion to participant in research? If so, what safeguards might be implemented to protect providers, while allowing important research to proceed?
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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 | 0.820 | 0.890 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.010 | 0.068 |
| Scholarly communication | 0.031 | 0.030 |
| Open science | 0.011 | 0.012 |
| Research integrity | 0.050 | 0.055 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".