Ethics and privacy issues of a practice-based surveillance system: need for a national-level institutional research ethics board and consent standards.
Bibliographic record
Abstract
OBJECTIVE: To describe the challenges the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) experienced with institutional research ethics boards (IREBs) when seeking approvals across jurisdictions and to provide recommendations for overcoming challenges of ethical review for multisite and multijurisdictional surveillance and research. BACKGROUND: The CPCSSN project collects and validates longitudinal primary care health information (relating to hypertension, diabetes, depression, chronic obstructive lung disease, and osteoarthritis) from electronic medical records across Canada. Privacy and data storage security policies and processes have been developed to protect participants' privacy and confidentiality, and IREB approval is obtained in each participating jurisdiction. Inconsistent interpretation and application of privacy and ethical issues by IREBs delays and impedes research programs that could better inform us about chronic disease. RESULTS: The CPCSSN project's experience with gaining approval from IREBs highlights the difficulty of conducting pan-Canadian health surveillance and multicentre research. Inconsistent IREB approvals to waive explicit individual informed consent produced particular challenges for researchers. CONCLUSION: The CPCSSN experience highlights the need to develop a better process for researchers to obtain timely and consistent IREB approvals for multicentre surveillance and research. We suggest developing a specialized, national, centralized IREB responsible for approving multisite studies related to population health research.
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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 | 0.766 | 0.719 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.017 | 0.031 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.011 | 0.014 |
| Research integrity | 0.015 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".