Supporting learning health systems through patient-oriented practice-based research: A provincial collaboration
Bibliographic record
Abstract
Learning health systems are promoted as solutions in Canada to bridge the disconnect between research and care delivery by integrating applied research and evidence supports within healthcare. Patients and clinicians see and experience healthcare system gaps and are therefore uniquely positioned as co-producers and partners in research to advance learning health systems. Practice-based research programs provide point-of-care healthcare professionals with training, mentorship, and nominal seed funding to conduct small research projects in their clinical contexts to address gaps in practice and care. Patient-oriented research engages patients, caregivers, and family with lived experience as partners in the process of identifying gaps, generating knowledge, and applying evidence to inform healthcare delivery. This article describes the benefits gained from unifying patient-oriented research programs in British Columbia, Canada, under a provincial collaboration to standardize practice and advance collective priorities, including the foundation to cultivate and support learning health systems transformation.
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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 | 0.039 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".