Ethics review as a component of institutional approval for a multicentre continuous quality improvement project: the investigator's perspective
Bibliographic record
Abstract
BACKGROUND: For ethical approval of a multicentre study in Canada, investigators must apply separately to individual Research Ethics Boards (REBs). In principle, the protection of human research subjects is of utmost importance. However, in practice, the process of multicentre ethics review can be time consuming and costly, requiring duplication of effort for researchers and REBs. We used our experience with ethical review of The Canadian Perinatal Network (CPN), to gain insight into the Canadian system. METHODS: The applications forms of 16 different REBs were abstracted for a list of standardized items. The application process across sites was compared. Correspondence between the REB and the investigators was documented in order to construct a timeline to approval, identify the specific issues raised by each board, and describe how they were resolved. RESULTS: Each REB had a different application form. Most (n = 9) had a two or three step application process. Overall, it took a median of 31 days (range 2-174 days) to receive an initial response from the REB. Approval took a median of 42 days (range 4-443 days). Privacy and consent were the two major issues raised. Several additional minor or administrative issues were raised which delayed approval. CONCLUSIONS: For CPN, the Canadian REB process of ethical review proved challenging. REBs acted independently and without unified application forms or submission procedures. We call for a critical examination of the ethical, privacy and institutional review processes in Canada, to determine the best way to undertake multicentre review.
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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 | 0.713 | 0.694 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.022 | 0.046 |
| Scholarly communication | 0.035 | 0.013 |
| Open science | 0.013 | 0.024 |
| Research integrity | 0.027 | 0.047 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".