Meeting the Privacy Requirements for the Development of a Multi-Centre Patient Registry in Canada: The Rick Hansen Spinal Cord Injury Registry
Bibliographic record
Abstract
Privacy legislation addresses concerns regarding the privacy of personal information; however, its interpretation by research ethics boards has resulted in significant challenges to the collection, management, use and disclosure of personal health information for multi-centre research studies. This paper describes the strategy used to develop the national Rick Hansen Spinal Cord Injury Registry (RHSCIR) in accordance with privacy statutes and benchmarked against best practices. An analysis of the regional and national privacy legislation was conducted to determine the requirements for each of the 31 local RHSCIR sites and the national RHSCIR office. A national privacy and security framework was created for RHSCIR that includes a governance structure, standard operating procedures, training processes, physical and technical security and privacy impact assessments. The framework meets a high-water mark in ensuring privacy and security of personal health information nationally and may assist in the development of other national or international research initiatives.
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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 | 0.161 | 0.141 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".