Using patient feedback to improve integrated care through online platform - Care Opinion.
Bibliographic record
Abstract
"Who is this networking session for? People interested in hearing about and discussing Care Opinion Canada, a platform that uses real time and transparent processes to support interaction between those receiving care in primary care or continuing care settings, and those providing it. Care Opinion Canada is being introduced initially in the province of Alberta by Imagine Citizens Network (ICN), led by volunteers who are working collaboratively with Care Opinion UK who developed the platform two decades ago. The goal is to give people a voice as well as improve the quality and integration of care. Who are you involving and engaging with? In addition to working with the originators of the Care Opinion platform in the UK, we are working closely with leaders in the primary care and continuing care sectors. This initiative in Alberta is directly the result of ICN introducing and championing the idea in the province. It has been supported by the Health Quality Council of Alberta, the Institute for Health Economics, the Primary Care Alliance and the Continuing Care Association.. This work to earn support and funding has been done by a small cadre of devoted volunteers within Imagine Citizens Network who over the past 3 years have met with several hundred individuals and organizations, participated in government-led consultations, and advocated for funding for Care Opinion.. What are we going to do? The platform is now online, undergoing beta testing in three adopter sites so we will show participants what it looks like and how it works. In 2024, implementation of Care Opinion in Alberta was made possible by funding from ICN, the Ministry of Health and Alberta Innovates. Working with the UK team and recently hired staff, we are adapting the application for novel sectors i.e. primary care and continuing care. What is the question you want to ask international colleague or problem you want their help to solve? Discussion in the session will be shaped by the interests of people in the room and their specific contexts and questions. Discussion points may include How can feedback from lived experiences of people as they seek health be embraced as part of a learning health system to support integrated care? What additional information would participants need they need in order to consider implementing this innovation (Care Opinion) in their own context?
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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 | 0.034 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".