Research protocol of the Laval-ROSA Transilab: a living lab on transitions for people living with dementia
Bibliographic record
Abstract
BACKGROUND: The Laval-ROSA Transilab is a living lab that aims to support the Laval Integrated Health and Social Services Centres (Quebec, Canada) in consolidating the Quebec Alzheimer Plan. It aims to improve care transitions between different settings (Family Medicine Groups, home care, and community services) and as such improve the care of people living with dementia and their care partners. Four transition-oriented innovations are targeted. Two are already underway and will be co-evaluated: A) training of primary care professionals on dementia and interprofessional collaboration; B) early referral process to community services. Two will be co-developed and co-evaluated: C) developing a structured communication strategy around the dementia diagnosis disclosure; D) designation of a care navigator from the time of dementia diagnosis. The objectives are to: 1) co-develop a dashboard for monitoring transitions; 2) co-develop and 3) co-evaluate the four targeted innovations on transitions. In addition, we will 4) co-evaluate the impact and implementation process of the entire Laval-ROSA Transilab transformation, 5) support its sustainability, and 6) transfer it to other health organizations. METHODS: Multi-methods living lab approach based on the principles of a learning health system. Living labs are open innovation systems that integrate research co-creation and knowledge exchange in real-life settings. Learning health systems centers care improvement on developing the organization's capacity to learn from their practices. We will conduct two learning cycles (data to knowledge, knowledge to practice, and practice to data) and involve various partners. We will use multiple data sources, including health administrative databases, electronic health records data, surveys, semi-structured interviews, focus groups, and observations. DISCUSSION: Through its structuring actions, the Laval-ROSA Transilab will benefit people living with dementia, their care partners, and healthcare professionals. Its strategies will support sustainability and will thus allow for improvements throughout the care continuum so that people can receive the right services, at the right time, in the right place, and from the right staff.
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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 | 0.074 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.089 | 0.020 |
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".