Advancing Learning Health Systems: A Preliminary Model of Learning at a Large Canadian Hospital
Bibliographic record
Abstract
The ability to learn is central to innovation and performance across industries. While learning drivers are well-articulated (experimentation, risk-taking, decision-making, dialogue, and interaction), they receive little attention in healthcare research. This case study explored the nature of learning within Trillium Health Partners (THP), the largest hospital system in Canada by patient volume. Formal leaders at THP completed the Organizational Learning Capability Survey. A latent profile analysis (LPA) identified the number and nature of distinct learning profiles. Semi-structured interviews were conducted to systematically explore the factors enabling or stifling organizational learning. N=231 leaders completed the survey. LPA identified two distinct learning profiles: one with high perceived learning capability and another with moderate capability. Interviews with leaders (n=12) from both groups described the factors that influence organizational learning (or a lack thereof). This systematic understanding of how learning occurs—or fails to occur—in healthcare organizations will support the development of evidence-based processes and programs that foster the learning necessary to develop innovative delivery models at scale.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".