Optimizing patient engagement to enhance a learning health system
Bibliographic record
Abstract
Background: Patient and family advisors have served as an integral part of a collaborative, province-wide learning health system in Alberta for more than a decade, contributing to evidence generation, knowledge mobilization and research activities focused on improving patient outcomes. Objective: This paper describes how Alberta Health Services (AHS) and the Strategic Clinical Networks™ (SCNs™) (i) embedded patient engagement and patient-oriented research in health services innovation and improvement, including project planning, co-design, execution and decision-making, (ii) created opportunities for patient advisors to participate in leadership committees, research panels and keynote addresses, (iii) co-designed engagement practices, resources and supports with patients and community partners, and (iv) applied a mixed-methods approach for assessing engagement effectiveness. Methods: AHS patient advisors collaborated with provincial partners and researchers, including the Alberta Strategy for Patient-Oriented Research (SPOR) Support Unit (AbSPORU) Patient Engagement Team, to co-design and pilot a standardized set of patient and family engagement indicators (PFE-Is) that could be used to evaluate engagement effectiveness and improve current practices. Through surveys and consultations with key interest holders, the team established a baseline for effective engagement and built consensus for patient engagement priorities, recommendations, and actions to improve patient and family engagement. Results: Five themes emerged from consultations with advisors and AHS staff: supports for engagement, learning together, diversity of perspectives, the role of advisors, and evaluating meaningful patient engagement. Recommendations and actions to strengthen patient engagement emerged that build on existing practices and supports, and include opportunities to improve resources, foster inclusivity, and promote collaborative learning opportunities. Discussion: The evidence-based PFE-Is and survey are ready for implementation across Alberta's health system to monitor and evaluate patient and family engagement, gather feedback from advisors and staff, and refine current strategies and practices. Continued collaboration with patient and family advisors is expected to support progress as a learning health system and strengthen the ability of provincial health agencies to generate actionable insights, drive improvements, and deliver high quality, patient-centred care.
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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 | 0.044 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".