Barriers and enablers to secondary use of multi-regional linked administrative health data for three distinct user groups: academic researchers, health-system knowledge users, and private sector researchers
Bibliographic record
Abstract
Objective and ApproachWe analyzed barriers and enablers to accessing pan-Canadian health sector data and data analytic services for three distinct user groups: 1) academic researchers, 2) health-system knowledge users (KUs), and 3) private sector researchers. Specifically, we a) conducted a legislative and policy scan regarding use of health administrative data for research, quality improvement, and health system management and b) systematically consulted with data centre directors and staff, privacy and legal experts, knowledge users (KUs), private sector entities, and university and government officials. ResultsKUs and private sector organizations face unique barriers because structures and regulations related to data access and use are set at multiple university and government institutions and are primarily designed for academic research. Additionally, data repositories often have specific policy/legislative barriers to working with KUs and/or private sector researchers. However, all groups face common issues including variations in regional regulations and policies making harmonized and streamlined processes challenging; lengthy data access and ethics review processes; and regional variations in data availability. Enablers included diverse expertise and initiatives across Canada such as: initiatives to streamline ethics reviews for multi-regional research, expand data holdings, increase standardization and harmonization, and implement systems for federated analyses. ConclusionsAccess to health system data and analytics in Canada varies by user group and across regions. Although each user group faces unique challenges, they also all have many barriers and enablers in common. ImplicationsImproving health system data access for one use group has the potential to benefit the others.
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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 | 0.092 | 0.199 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".