The Raison d'Être of Mutual Recognition: An Analysis of the 2015 Reform to Research Ethics Review Policies, Processes and Problems in Québec
Bibliographic record
Abstract
Ethics review is a pre-requisite to conducting research involving humans in Canada, and indeed in most international jurisdictions.The Tri-Council Policy Statement on Ethical Conduct for Research Involving Humans (TCPS2) serves as the national policy framework for research ethics review in Canada, and outlines three potential oversight models: independent, delegated and reciprocal.While the independent model preserves institutional oversight of research, it contributes to a duplicative system that can unduly delay research and impose barriers to research collaboration.This analysis centres on a 2015 reform to the policy model of research ethics review for collaborative, multi-site studies in the province of Québec.Informal interviews with key informants supplemented a document analysis of provincial research ethics policies using the comparative framework proposed by Lavis and colleagues.Consolidating bureaucratic structures and preserving locally-relevant review studies that span multiple sites remain among the most pressing challenges to transitioning from an independent model, and could provide reference for other provinces that have, or are currently in the process of such a transition.2015 Reform to Research Ethics Review in Québec Rahimzadeh Key Messages• Despite significant growth in the number and types of scientific research collaboration across centres (and in some instances, across borders), the procedural inefficiencies of the independent model greatly challenge such collaboration.This is most notable in fields of research that require multi-site collaboration, thus underscoring a scientific rationale for reform from the researcher's perspective. Rahimzadeh Key factors Values Values• Significant social and scientific value is placed on the translation of clinical research into practice.• Protections for humans involved in research are internationally codified in the United Declaration on Human Rights, Declaration of Helsinki and Council of International Organizations of Medical Sciences (CIOMS), for example.• Respect for persons, concern for welfare, beneficence and justice are dominant ethical principles in the conduct of research involving humans.• Privacy protection, data security, and data sharing are contemporary concerns of biomedical research participants and researchers.• Scientific and ethical imperative to collaborate in research requires similar degrees of collaboration among REBs and other forms of ethics governance.• Procedural efficiency of ethics review enables timely clinical innovation and translation.
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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 | 0.085 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.028 | 0.019 |
| Scholarly communication | 0.023 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".