A roadmap for navigating child health research data sharing across Canada and beyond – building on UCAN CAN-DU
Bibliographic record
Abstract
BACKGROUND: Sharing health data within and across jurisdictions is important for research and improving healthcare quality; however, researchers, governments and funders must balance the benefits of data sharing with data privacy. Though frameworks exist to guide data sharing it can be difficult to translate these into practice. Therefore, our aim was to create a practical example of data sharing for researchers in pediatric rheumatology. METHODS: We utilized expert consultation with leaders in child health, genomics, rheumatology, bioethics, privacy and records, bioinformatics, and legal counsel to better understand barriers and enablers for sharing of health data. We used these barriers to frame the learnings of UCAN CAN-DU (Understanding Childhood Arthritis Network Canada-Netherlands Personalized Medicine Network in Childhood Arthritis and Rheumatic Diseases) which is a collaboration that collects and shares phenotypic, genomic, health economic and patient reported data across centers in Canada and the Netherlands in order to provide a real-life, practical example of data sharing across borders in pediatric rheumatology. RESULTS: Barriers to data sharing include lack of standardized consent, ethics review processes for multi-site projects, developing data governance frameworks aligned with institutional and regulatory requirements, differing data standards and a lack of interoperability, and managing data access. UCAN CAN-DU provides lessons for navigating these barriers through standardized consent forms, centralized ethics review and reciprocity agreements; building a network to support data interoperability and harmonization of procedures; documentation to support data sharing, including legal agreements, utilization of a secure healthcare data storage compute facility, and a data access advisory committee with clear policies for secondary data use. CONCLUSION: We have shared how UCAN CAN-DU navigated barriers to data sharing, providing an example of data sharing for researchers in pediatric rheumatology. This work highlights the importance of research networks in establishing interoperability including minimal data sets, standard operating procedures, and institutional legal/contracts partnerships that ultimately support data sharing. The barriers and enablers presented are broadly applicable across countries and provide direction on areas for future research and initiatives to foster data sharing.
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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 | 0.154 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.021 | 0.014 |
| Scholarly communication | 0.028 | 0.019 |
| Open science | 0.011 | 0.035 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".