Commentary on “Regulatory Support Improves Subsequent IRB/REC Approval Rates in Studies Initially Deemed Not Ready for Review: A CTSA Institution’s Experience”
Bibliographic record
Abstract
In response to researcher concerns a number of initiatives have been developed to support individual researchers seeking ethics review and approval. In this issue, Sonne et al. (2017) outline an example of an intervention to support researchers, which they refer to as a Regulatory Knowledge Support (RKS) service. While the study points to potential benefits, other studies have not had the desired impact on key performance measures. There is a need to develop a community of practice and expand the burgeoning evidence base regarding what interventions work, for whom, and under what circumstances. Advancing the research agenda requires: the development of theoretical models for intervention design and evaluation; developing consensus on key data for collection and measures of effectiveness; conducting evaluations using the strongest possible study designs, and; publishing the findings of evaluations.
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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 | 0.212 | 0.434 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.122 |
| 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".