Enabling Connected Care with a Person-Centred Data Foundation
Bibliographic record
Abstract
Having the right information at the right time and at the fingertips of the right individuals is not just a necessity for a well-functioning healthcare system but it is also the difference between life and death for Canadians. It is particularly critical to enable improved access to and quality of care for equity-deserving individuals because these data eliminate blind spots for clinicians, policy makers and system planners. The COVID-19 pandemic put a spotlight on the health data challenges that exist across Canada and the tangible impact those have on the healthcare system's ability to meet the needs of underserved populations. It sparked unified urgency at the federal and provincial/territorial levels to build a learning health system powered by connected health data for clinical care, patient access, care organization operations, health system use and population/public health. Person-centric data content standards will lie at the foundation of Canada's learning health system, enabling the creation and exchange of data.
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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 | 0.086 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.007 | 0.044 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.023 | 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".