Patient-Centred Innovations for Persons with Multimorbidity: funded evaluation protocol
Bibliographic record
Abstract
BACKGROUND: The high prevalence of multimorbidity necessitates rethinking of the health care system. The overarching goal of the Patient-Centred Innovations for Persons with Multimorbidity program is to build on existing structures and find and evaluate patient-centred innovations relevant to multimorbidity. METHODS: We describe the protocol for a proposed multijurisdictional (Quebec and Ontario) concurrent triangulation mixed-methods study. In both provinces, a qualitative descriptive study will be used to explore innovations in patient-centred multimorbidity care. Two randomized controlled trials, 1 in either province, will evaluate the innovations in a wait-list-controlled design using patient-reported outcomes. An additional control group, matched on age, sex, enrolment/index date (± 3 mo) and propensity score, will be created with the use of health administrative data. Patients will be 18-80 years of age and will have 3 or more chronic conditions. The innovations will have elements of relevance to multimorbidity care, patient-centred partnerships and integration of care. The primary outcome measures will be 2 patient-reported outcomes: patient education and self-efficacy. Secondary outcomes will include patient-reported health status, quality of life, psychological distress and health behaviours, and costs of care. INTERPRETATION: ClinicalTrials.gov, no NCT02789800 (Quebec Trial), NCT02742597 (Ontario Trial).
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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 | 0.087 | 0.067 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.074 | 0.014 |
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".