Research Ethics Oversight for Multi-jurisdictional Clinical Trials in Canada: A Historical Perspective to Inform Future Direction
Bibliographic record
Abstract
As a condition of funding from the Canadian Tri-Agencies, researchers and institutions are expected to adhere to the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (TCPS) and research projects must obtain approval from a Research Ethics Board (REB). Yet there are a limited number of frameworks and standards to guide the conduct and decisions of REBs in Canada and these are most often voluntary. Delays and increased costs resulting from the lack of a common or coordinated approach to REB review for multi-jurisdictional clinical trials is a long-standing issue in the Canadian research environment. Formal reviews and recommendations as early as 2006 call for accreditation or qualification for REBs, use of common forms and templates, and federal leadership to harmonize processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.134 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.025 | 0.050 |
| Scholarly communication | 0.030 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.012 | 0.027 |
| 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; 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".