Establishing a learning agenda for learning health system implementation and research in Canada
Bibliographic record
Abstract
Health systems in Canada struggle to generate and use knowledge to improve equity-centred quadruple aim measures, resulting in care that is misaligned with local contexts. Learning Health Systems (LHS) offer a solution by aligning real-time evidence, informatics, patient-provider partnerships, and institutional strategies to support continuous improvements in care. Despite their potential, LHS initiatives in Canada remain siloed and lack harmonized leadership, knowledge exchange, and capacity building. To address these gaps, the Learning Health Hub was established to foster collaboration, disseminate best practices, and enhance collective capacity for LHS in Canada. In June 2024, the Learning Health Hub hosted its inaugural virtual symposium. This event brought together partners, researchers, health professionals, system operators, and policymakers from across Canada interested in LHS work. The symposium aimed to map assets and build momentum for larger-scale impact in LHS. Participants engaged in generative activities to define challenges and co-create solutions, resulting in the identification of key learning priorities. Three learning themes were identified: Patient, Caregiver, and Community Partnership; Enabling Environments; and Benchmarking and Evaluation. By advancing these themes, the Learning Health Hub aims to drive meaningful, sustainable change and improve healthcare quality and outcomes.
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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 | 0.102 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.054 | 0.040 |
| Scholarly communication | 0.039 | 0.012 |
| Open science | 0.010 | 0.036 |
| Research integrity | 0.016 | 0.025 |
| Insufficient payload (model declined to judge) | 0.010 | 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".