Modernize the Healthcare System: Stewardship of a Strong Health Data Foundation
Bibliographic record
Abstract
The Canadian Institutes of Health Research - Institute of Health Services and Policy Research (IHSPR) has published its Strategic Plan 2021-2026 (CIHR IHSPR 2021) and, as members of the Expert Advisory Group for a Pan-Canadian Health Data Strategy, we are providing commentary on the second strategic priority of IHSPR's Strategy related to health data and digital health. Systemic barriers have prevented the timely and effective collection, sharing and use of health data in Canada. Many of these systemic barriers relate to the fragmented health data foundation, lack of coordinated data governance and a risk-averse culture. As IHSPR mobilizes its strategic plan, it will be important to consider and address these factors head-on to contribute to a stronger health data foundation that would help achieve both IHSPR's strategic objectives and meaningfully contribute to elevating Canada's health data ecosystem.
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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 | 0.193 | 0.279 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.017 | 0.057 |
| Scholarly communication | 0.044 | 0.030 |
| Open science | 0.008 | 0.031 |
| Research integrity | 0.019 | 0.048 |
| Insufficient payload (model declined to judge) | 0.006 | 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".