Alberta's Data Mobilization Strategy: Leveraging Linked Data for Innovation
Bibliographic record
Abstract
IntroductionThe Province of Alberta maintains a mature data ecosystem with linkable data dating back over 30 years. The population-based nature of the data makes this a valuable asset for driving analytics to support health system innovation, with a focus on improving health outcomes and quality of life. Objectives and ApproachAlberta Health has created the Secondary Use Data Access (SUDA) initiative to leverage its administrative health data. SUDA envisions strengthening partnerships between the public and private sectors with two main access approaches. The first is direct access to de-identified data held within the Alberta Health data warehouse by key health system stakeholders (e.g. academic instituions, Health Quality Council of Alberta, regulatory colleges). The second is indirect access to private and not-for-profit stakeholders, using a safe haven approach. Indirect access is achieved through private sector investments to a trusted third party that hires analysts to be placed within Alberta Health. ResultsStaffing agreements and privacy impact assessments have been drafted to support the work. The indirect access route includes a multiple stakeholder steering committee to vette and prioritize projects. Private and not-for-profit stakeholders do not have access to the data, but rather receive access to aggregate data and statitstical models. All disclosures are done by Alberta Health staff to ensure compliance with Alberta's Health Information Act. Direct access has been established for the Alberta Medical Association as part of a long standing data sharing agreement, with access restricted to de-identified data only. To date, seven industry proposals for analytics have been received and are currently being actioned. Conclusion/ImplicationsThe Secondary Use Data Access initiative uses a safe haven approach to leveraging data. It reduces the need to provision data outside of the data warehouse and allows for better monitoring of access and use of data. The approach provides assurances that people's health information is secure.
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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 | 0.059 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.022 | 0.007 |
| Open science | 0.009 | 0.023 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".