Principles and operational model for governing Diabetes Action Canada's data repository for patient-oriented research
Bibliographic record
Abstract
Introduction Diabetes Action Canada is developing a data repository and registry of potential research participants to support research, QI, and service to improve diabetes care. Central to the repository are pseudonymised linkable electronic medical records (EMRs) from family practices that are participating in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Objectives and Approach We sought to develop an information governance process that would engender the trust of patients and the health care professionals (HCPs) that their EMR data were being managed responsibly in the best interests of patients living with diabetes. Following an extensive literature review, we developed a principles-based governance framework and operational model, with a strong focus on patient participation in the governance process. We recruited patients through our pre-existing patient advisory circles and physicians through our partners in CPCSSN. In January 2018, we held a training workshop for Research Governing Committee (RGC) members. Results We identified eight values-based principles to guide our governance process: transparency; accountability; following the rule of law; integrity of purpose, science and ethics; participation and inclusiveness; impartiality and independence; effectiveness; efficiency and responsiveness; and reflexivity and continuous quality improvement of process. Patients represent 50% of RGC members and HCPs 20%. Patient members provide their perspectives on: goals and outcomes of the research; the benefits and burdens among people living with diabetes; and the communication preferences of patients around recruitment. HCPs provide a deep understanding of the settings and systems in which care is provided to ensure contextual integrity of the research. Two researchers and one person with bioethics expertise provide technical and ethics perspectives on data requests. Conclusion/Implications Governance must go beyond legal compliance to ensure a ’social licence’ for the use of the data. In part, we address this through our guiding principles, our emphasis on patient and healthcare provider perspectives, and focus on research that is scientifically sound, ethically robust and in the public interest.
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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 | 0.253 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.016 | 0.049 |
| Scholarly communication | 0.039 | 0.013 |
| Open science | 0.010 | 0.016 |
| Research integrity | 0.010 | 0.010 |
| 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; 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".