Ottawa Statement does not impede randomised evaluation of government health programmes
Bibliographic record
Abstract
In this issue of JME, Watson et al call for research evaluation of government health programmes and identify ethical guidance, including the Ottawa Statement on the ethical design and conduct of cluster randomised trials, as a hindrance. While cluster randomised trials of health programmes as a whole should be evaluated by research ethics committees (RECs), Watson et al argue that the health programme per se is not within the researcher’s control or responsibility and, thus, is out of scope for ethics review. We argue that this view is wrong. The scope of research ethics review is not defined by researcher control or responsibility, but rather by the protection of research participants. And the randomised evaluation of health programmes impacts the liberty and welfare interests of participants insofar as they may be exposed to a harmful programme or denied access to a beneficial one. Further, Watson et al’s claim that ‘study programmes … would occur whether or not there were any … research activities’ is incorrect in the case of cluster randomised designs. In a cluster randomised trial, the government does not implement a programme as usual. Rather, researchers collaborate with the government to randomise clusters to intervention or control conditions in order to rigorously evaluate the programme. As a result, equipoise issues are triggered that must be addressed by the REC.
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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 | 0.070 | 0.258 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.156 | 0.089 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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".