Building a Canadian Data Platform under the Strategy for Patient-Oriented Research
Bibliographic record
Abstract
Background with rationaleThe diversity of Canada’s health systems and policies offers fertile ground for natural experiments, comparative analysis, and sharing of best practices. Investments over the last 25 years, measured in hundreds of millions of dollars, have created provincial centres with rich health and social data, national health surveys and more recently, clinical and other data. While much progress has been made within individual provinces and territories, challenges with comparability and timely access to data between jurisdictions remain. Main AimOur aim is to develop a distributed network that facilitates and accelerates multi-jurisdictional research. Methods/ApproachOur team includes data stewards, clinicians, decision-makers, patients, and researchers who are recognized as international leaders in data systems, access governance and engagement. The objectives for the Canadian Data Platform are to: create a data access support system that helps navigate multi-jurisdiction requests; to harmonize and validate definitions for important chronic diseases and other key variables to facilitate multi-jurisdictional research; to continue to expand the sources and types of data and linkages available; to develop the technology infrastructure required to improve the data access request process, data documentation, and re-use of algorithms; to create supports for advanced analytics and infrastructure for data collection and analysis; to establish strong partnerships with patients and the public and with Indigenous communities; and to build strong governance and enable national coordination. ResultsOur Data Access Support Hub will open in the fall of 2019, at which time we will have an inventory of data available across our network, and the beginnings of a catalog of algorithms and harmonized data. ConclusionBuilding cross-national resources to support multi-jurisdictional research can be challenging in places where there are multiple levels of governance of health and social services. Our network is one example of an approach to addressing these challenges.
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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 | 0.099 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.010 | 0.022 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.013 |
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".