A framework to facilitate interprovincial sharing of secondary health data in Canada
Bibliographic record
Abstract
IntroductionThe use of administrative health data can generate knowledge to improve the delivery and outcomes of health care. Yet, the sharing and use of secondary health data presents concerns given these data were not collected for health research purposes. The sharing of patient-level health data across Canadian provinces is uncommon. Objectives and ApproachThe Maritime SPOR SUPPORT Unit (a patient-oriented research unit serving the three Canadian Maritime Provinces of New Brunswick, Nova Scotia, and Prince Edward Island) struck a Working Group to develop a conceptual framework for the interprovincial sharing of secondary health data for research purposes. Membership comprised a researcher, two privacy managers/officers, and a manager of research ethics. The framework sought to: (1) facilitate researchers’ understanding of the foundational elements (legal/ethical) of interprovincial data sharing for health research; and (2) identify challenges and opportunities for improving sharing of data across the Maritime Provinces to support patient-oriented research. ResultsIn all three Maritime provinces, de-identified personal health information may be used for approved health research purposes, with each province having its own data holdings and repositories. Applying the applicable governance principles and regulations (i.e., the ethical governance of research involving human subjects and the legal governance of health information) and drawing on best practices nationally and internationally, a framework was developed to incorporate and address the various aspects of sharing and using health data across provinces for the purposes of health research. The resultant framework discusses when and how the legal and ethical frameworks apply, the de-identification of data, degrees of data sharing, and information governance. It also identifies challenges and opportunities to moving forward with interprovincial data sharing. Conclusion/ImplicationsDevelopment of this framework was the first phase of a multi-phase approach to move towards improved interprovincial data sharing for patient-oriented research. Cross-provincial sharing and linkage of data can lead to comprehensive, cost-effective, and multi-disciplinary research that benefits patients, the health system, and the public at large.
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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 | 0.091 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.009 | 0.021 |
| Research integrity | 0.004 | 0.003 |
| 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".