Addressing multi-region data linkage needs through data sharing agreements – Three Canadian initiatives.
Bibliographic record
Abstract
ObjectivesData sharing and administrative data access for multi-jurisdictional research must accommodate all local requirements for the protection of personal information and personal health data. Navigating the different safeguards for data sharing found in all relevant Canadian provincial and territorial legislation requires exploring innovative, privacy compliant solutions. ApproachHealth Data Research Network Canada (HDRN Canada) and one of its provincial data centre partners, New Brunswick Institute for Research, Data and Training (NB-IRDT), are undertaking unconventional approaches to data sharing that balance legislative compliance and data access needs. Three approaches are being pursued: harmonizing data sharing agreements between a national longitudinal study and 10 provincial data centres; facilitating 4 individual data sharing agreements with one regional cohort of a nationwide study; and coordinating data sharing between 4 provincial government departments and one national data centre. Each approach has unique features that presented problems needing innovative resolutions. ResultsThe experiences and challenges of these three approaches to data sharing for multi-regional research have been perplexing but ultimately are leading to productive outcomes. Learnings acquired enable partners to move beyond anecdotal perceptions of data sharing limitations to practical solutions that can be applied in future data sharing partnerships. The ongoing project management approach of these initiatives identified a core set of somewhat predictable devices to ensure the advancement of simultaneous multiple regional data sharing agreements. These include the assignment of a project coordinator role, clear and ongoing communication, and the ability to negotiate and manage expectations. More innovative, however, is the roadmap that evolved, laying out the necessary (previously unanticipated) measures and sequence in which they should occur for the most expedient outcome. ConclusionTo harness the potential of multi-regional research in Canada, provinces and territories must identify feasible and efficient data sharing solutions. The identification of adoptable and replicable legislatively compliant data sharing practices will increase potential data accessibility. This will increase available data and data platforms for research informed policy decisions.
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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 | 0.094 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.008 | 0.023 |
| Research integrity | 0.005 | 0.005 |
| 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; 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".