Ethical and Legal Considerations for Canadian Registries
Bibliographic record
Abstract
This section summarizes the ethical and legal considerations that will impact the creation and operation of neurological disease registries in Canada.This document is not meant to provide legal or ethical advice.In order to ensure that applicable laws and organizational policies are adhered to in an appropriate manner, it is recommended that legal advisors and relevant organizational representatives be consulted.For registries to succeed, it is critical to proactively consider legal and ethical issues such as consent and privacy.Additional ethical and legal considerations include: the involvement of Aboriginal people and their communities, languages and communication; setting up of biobanks; data management; data ownership; and conducting transparent registry operations.The Belmont Report -Ethical Principles and Guidelines for the protection of human subjects of research 6 and the Government of Canada Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans (TCPS-2) 7 should be referred to for the ethical principles that need to be considered during the creation of disease registries.In addition, Registries for Evaluating Patient Outcomes: A User's Guide 5 produced by The Agency for Healthcare Research and Quality provides useful information.However, this document presents perspectives and reviews legislation particularly relevant to the United States which differ in some respects from Canadian law and research policies and practice.In preparation of this guideline, we examined relevant Canadian and international literature as well as Canadian policy and legislation.We also consulted with Canadian privacy officers and specialists in research ethics.Finally, topic themes and issues were discussed with patients and families in project focus groups. BACKGROUNDIn Canada, Research Ethics Boards (REBs) are the equivalent of what is more commonly known as Institutional Review Boards (IRBs) in other jurisdictions.The TCPS-2 describes the authority, mandate and accountability of REBs.In some cases, provincial and federal legislation also applies.
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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 | 0.070 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".