Co-Designing an Integrated Care Network with People Living with Parkinson’s Disease: A Heterogeneous Social Network of People, Resources and Technologies
Bibliographic record
Abstract
As part of the iCARE-PD project, a multinational and multidisciplinary research endeavour to address complex care in Parkinson's disease, a Canadian case study focused on gaining a better understanding of people living with Parkinson's disease (PwP) experiences with health and medical services, particularly their vision for a sustainable, tailored and integrated care delivery network. The multifaceted nature of the condition means that PwP must continuously adapt and adjust to every aspect of their lives, and progressively rely on support from care partners (CP) and various health care professionals (HCP). To envision the integrated care delivery network from the perspective of PwP, the study consisted of designing scenarios for an integrated care delivery network with patients, their CP and their HCP, as well as identifying key requirements for designing an integrated care delivery network. The results demonstrate that numerous networks interact, representing specific inscriptions, actors and mediators who meet at specific crossing points. This resulted in the creation of a roadmap and toolkit that takes into consideration the unique challenges faced by PwP, and the necessity for an integrated care delivery network that can be personalized and malleable so as to adapt to evolving and changing needs over time.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".