Navigating research ethics for multi-institutional, hospital-based research: Experiences from the Translating Emergency Knowledge for Kids (TREKK) initiative
Bibliographic record
Abstract
Health research ethics processes in Canada are guided by the Tri-council Policy Statement (TCPS 2); however, complications still arise when conducting multi-site studies involving research ethics applications to more than one institutional body. The TRanslating Emergency Knowledge for Kids (TREKK) needs assessment study, a low-risk cross-sectional survey in 32 hospitals across Canada, is described as one example of the complex process for obtaining Canada-wide research ethics approvals and illustrates the variation in interpreting and applying the TCPS 2 in different jurisdictions. The TREKK experience with health research ethics raises many questions about whether the TCPS 2 is founded on an aging model of research and advocates for a new, more flexible approach to multi-site research in the Canadian context.
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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 | 0.025 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.011 |
| 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".