Study Protocol – Creating an Asset Map of Health Data Platforms in Canada
Bibliographic record
Abstract
Statement of Problem Canada’s health research ecosystem includes multiple diverse data platforms across various disease domains. While enabling broad research, they often operate in silos with differing governance, standards, and technologies. A systematic mapping of these platforms is needed to identify gaps, overlaps, and opportunities for interoperability and collaboration. Both administrative and research data platforms are critical to Canada’s health data landscape, including CIHI , HDRN and GEMINI . Administrative platforms, such as CIHI, collect and standardize administrative and clinical data [1]. National networks, such as HDRN Canada connect provincial and territorial data centers to improve health outcomes and equity [2]. GEMINI, a research-oriented platform, aims to harmonize electronic health records for improved usage in clinical research [3]. Similarly, the ARCHIMEDES health data platform provides a federated environment for the integration of multi-modal research data to support discovery science [4]. However, the rapid proliferation of health data platforms across Canada has created challenges for interoperability, sustainability, and duplication. Many platforms operate with limited funding, differing data standards, and lack mechanisms for interoperability. These challenges create barriers in leveraging existing investments, aligning with national data strategies, and ensuring access to data resources across research domains. In response to this challenge, a national asset map will identify existing platforms, their connections, and opportunities to enhance interoperability, thereby fostering collaboration, sustainability, and reducing redundancy. Objective The objective is to create a national asset map of Canadian health data infrastructure, describing each platforms’ technical capabilities, governance, data standards, funders, cost models, and data modalities. Methods The methodology includes three steps: platform search, data extraction, and verification by platform representatives. Outcomes The project will create a national asset map documenting the functionalities of Canada‘s health data platforms, including their technical capabilities, governance models, and data standards. The resource will highlight each platform’s strengths and gaps, promote transparency, enhance collaboration, and help funders reduce redundancy by supporting complementary, non-duplicative investments.
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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 | 0.062 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.077 | 0.012 |
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".