Building a Learning Health System for Major Neurocognitive Disorders: The Creation of Regional Portraits as a Supporting Tool
Bibliographic record
Abstract
Context: Providing context-specific information and fostering reflective practice is essential to support a learning health system. Regional health boards in Quebec, Canada, have the responsibility to ensure implementation of the Quebec Alzheimer’s Plan (QAP) in primary, secondary, and tertiary care services but need support. As such, we have developed and disseminated regional portraits on dementia care. Objective: To describe the lesson learned and next steps on the development and presentations of regional portraits. Design: A multi-methods study with a participatory approach. Setting: Quebec, Canada. Population: Stakeholders (ministerial decision-maker and project managers, managers and clinicians from the 23 regional health board, and researchers/scientific advisors). Methods: We selected, with stakeholders, 9 indicators (prevalence, regular physician visits, emergency visits, and hospitalizations) and measured them in 2019-20. Also, we thematically analysed the last 3 years of ongoing QAP implementation evaluation reports and meetings. We combined these results to formulate, with stakeholders, key messages for each regional health board. Along with ministerial decision-maker and project managers, we presented these portraits to regional managers and clinicians. Real-time notes were taken using structured observation guide. A thematic analysis was performed. Results: The development of the portraits was facilitated by a strong and ongoing alliance between the stakeholders. Researchers and scientific advisors identified the most relevant data available; project managers ensured the formulated messages were meaningful to regional managers and clinicians; and policymakers ensured the collected data was useful to support the implementation of QAP. The regional managers and clinicians proposed ways to improve the portraits (i.e., adding indicators), and to regularly update and integrate these portraits into steering committees. Monitoring the indicators as precisely presents challenges, including regularly obtaining current data. Conclusion: This innovative project supports a learning health system for the care of persons with dementia. Specifically, it stimulates the emergence of regional and provincial innovations in dementia care to accomplish the implementation of the QAP, ensure its local appropriation and perpetuates sustainable transformed practices. Proposed solutions will guide the preparation of next iteration of regional portraits.
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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 | 0.053 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.014 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".