Patient-centred innovation for multimorbidity care: a mixed-methods, randomised trial and qualitative study of the patients’ experience
Bibliographic record
Abstract
BACKGROUND: Patient-centred interventions to help patients with multimorbidity have had mixed results. AIM: To assess the effectiveness of a provider-created, patient-centred, multi-provider case conference with follow-up, and understand under what circumstances it worked, and did not work. DESIGN AND SETTING: Mixed-methods design with a pragmatic randomised trial and qualitative study, involving nine urban primary care sites in Ontario, Canada. METHOD: Patients aged 18-80 years with ≥3 chronic conditions were referred to the Telemedicine IMPACT Plus intervention; a nurse and patient planned a multi-provider case conference during which a care plan could be created. The patients were randomised into an intervention or control group. Two subgroup analyses and a fidelity assessment were conducted, with the primary outcomes at 4 months being self-management and self-efficacy. Secondary outcomes were mental and physical health status, quality of life, and health behaviours. A thematic analysis explored the patients' experiences of the intervention. RESULTS: = 0.006). More providers and follow-up hours were associated with poorer outcomes. Five themes were identified in the qualitative study: valuing the team, patients feeling supported, receiving a follow-up plan, being offered new and helpful additions to their treatment regimen, and experiencing positive outcomes. CONCLUSION: Overall, the intervention showed improvements only for patients who had an annual income of ≥C$50 000, implying a need to address the costs of intervention components not covered by existing health policies. Findings suggest a need to optimise team composition by revising the number and type of providers according to patient preferences and to enhance the hours of nurse follow-up to better support the patient in carrying out the case conference's recommendations.
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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 | 0.072 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".