Accessing Health and Health-Related Data in Canada: The Expert Panel on Timely Access to Health and Social Data for Health Research and Health System Innovation
Bibliographic record
Abstract
Key Findings For effective research with health and health-related data, disparate sources of data must be brought together. Providing these data in an “analysis-ready” format, thereby allowing statistical relationships or patterns to be derived, is a central methodological challenge. Evidence shows that timely access to data enables significant high-quality research that can have far-reaching effects for health care and the overall health of Canadians. The risk of potential harm resulting from access to data is tangible but low. The level of risk can be further lowered through effective governance mechanisms. Timely access to data is hindered by variable legal structures and differing interpretations of the terms identifiable and de-identified across jurisdictions. Instead of rigidly classifying data as either identifiable or non-identifiable, it is useful to view de-identification as a continuum and to adjust access controls accordingly. Evidence demonstrates that a shift is occurring among leading entities from a 'data custodianship' model to a 'data stewardship' model. Central to the success of this shift is the adoption of good governance practices, specifically in privacy governance, research governance, information governance, and network governance.
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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 | 0.209 | 0.251 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".