Regulatory Framework for Conducting Clinical Research in Canada
Bibliographic record
Abstract
Research in human subjects is at the core of achieving improvements in health outcomes. For clinical trials, in addition to the peer review of the results before publication, it is equally important to consider whether the trial will be conducted in a manner that generates data of the highest quality and provides a measure of safety for the participating subjects. In Canada, there is no definitive legislation that governs the conduct of research involving human subjects, but a network of regulations at different levels does provide a framework for both principal investigators and sponsors. In this paper, we provide an overview of the federal, provincial and institutional legislation, guidelines and policies that will inform readers about the requirements for clinical trial research. This includes a review of the role of the Food and Drug Regulations under the Food and Drugs Act and the Tri-Council Policy Statement (TCPS2), an overview of provincial legislation across the country, and a focus on selected policies from institutional research ethics boards and public health agencies. Many researchers may find navigation through regulations frustrating, and there is a paucity of information that explains the interrelationship between the different regulatory agencies in Canada. Better understanding the process, we feel, will facilitate investigators interested in clinical trials and also enhance the long-term health of Canadians.
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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 | 0.101 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".