Future of Multimorbidity Research: How Should Understanding of Multimorbidity Inform Health System Design?
Bibliographic record
Abstract
Many people living with chronic conditions have multiple chronic conditions. Multimorbidity is defined here as the co-existence of two or more chronic conditions, where one is not necessarily more central than the others. Multimorbidity affects quality of life, ability to work and employability, disability and mortality. Currently, clinicians have limited guidance or evidence as to how to approach care decisions for such patients. Understanding how to best care and design the health system for patients with multimorbidity may lead to improvements in quality of life, utilization of healthcare, safety, morbidity and mortality. The objective of this paper is to review the implications of multimorbidity for the design of health system and to understand the research needs for this population. The consideration of people with multimorbidity is essential in the design and evaluation of health systems. Fundamentally, people with multimorbidity should receive a patient — and family-centered approach to care throughout the health system, and understanding how to deliver this type of care in effective and efficient ways is an enormous challenge, and opportunity, for clinicians, researchers, and policy makers today.
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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 | 0.237 | 0.273 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.015 | 0.033 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.014 |
| 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".