Co‐creating an everyday language illustration of learning health systems alongside patient, caregiver, and community partners
Bibliographic record
Abstract
Introduction: Patients, caregivers, and community partners (PCC) can have a variety of roles in learning health systems (LHS), such as contributing their data from healthcare encounters to embedded, continuous engagement where they identify health system priorities, guide operational, research, and quality improvement decisions, and facilitate knowledge sharing and implementation. Despite many LHS models placing emphasis on PCC, little has been done to help members of the public understand what a LHS is or initiate dialogue about how they can learn more and become engaged. We brought together a national network of PCC to co-create an everyday language, arts-based resource for the public to learn what a LHS is and how it relates to patient care journeys. Methods: Thirteen PCC with LHS experience from across Canada attended two 2-h virtual workshops to generate ideas on how to better define LHS using everyday language, determine accessible ways to share this information, and co-design a comic strip that can be widely shared across diverse settings and communities. Results: We co-created a six-panel comic strip that depicts a relatable patient experience of waiting in an emergency department. The comic shows that in a LHS, patients are invited to contribute their perspectives about improving healthcare and support implementing and testing new ideas in clinical settings. Creating this comic was considered important for various reasons: to promote a common language around LHS, to build trust between health systems and the public, and to widen the community of PCC who are engaged in LHS activities. Conclusions: This comic is intended to build capacity for LHS culture, where the public can understand how continuous learning and improvement fit within health care, and learn about opportunities for engagement in LHS.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.026 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".