11.D. Workshop: Global Burden of Multimorbidity: from Epidemiology to Policy
Bibliographic record
Abstract
Abstract Background Ageing societies have become a growing phenomenon globally. One of the most frequent consequences of ageing is an accumulation of diseases, hence living with multiple conditions in advanced age has become the norm rather than the exception. Multimorbidity is usually defined as the coexistence of two or more chronic conditions. It is associated with increased disability and functional decline, polypharmacy, reduced quality of life, and increased health care costs. People with multimorbidity represent as well the most vulnerable population subgroup to severe consequences from the current pandemic. Aim The overall aim of the workshop will be to facilitate cross-national discussion about ongoing and completed research in public health and primary care, and to identify the next steps for key areas of multimorbidity research and policy. The specific objectives of this workshop will be three-fold: to discuss progress and findings that have already been achieved in respective jurisdictions and countries of the participating speakers; to facilitate collaboration through brainstorming and discussion to identify strategies to move multimorbidity research and policy forward; and to create concrete plans to ensure advances in multimorbidity research and knowledge can be achieved through cross-national partnerships, with potential implications for the prevention, clinical management and public health policies regarding multimorbidity. Structure The workshop will consist of four presentations by leading scholars in the field of multimorbidity research and policy. Specifically, Dr. Kathryn Nicholson (Western University, Canada) will provide a global overview on the epidemiology of multimorbidity and underlying risk factors and their impact on policy, across different world regions; Prof. Dr. Marjan van den Akker (Goethe University, Germany) will discuss different models of care as well as major health care challenges in the management of multimorbidity in primary care with focus on interactions (disease-disease, treatment-treatment, and treatment-disease) extrapolated from (disease specific) guidelines and the feasibility to apply these guidelines for and with patients who have multimorbidity; Dr. Iveta Nagyova (PJ Safarik University, Slovakia) will address the potential for behavioural interventions to improve the cost-effectiveness of public health policy for the prevention and management of multimorbidity; finally, Dr. Gauden Galea (WHO) will provide a global perspective on current public health policies to tackle the growing burden of multimorbidity both in high-income and low-resource settings. Following the presentations by the four speakers, an open discussion will give attendees the possibility to share their opinions regarding challenges and opportunities in the prevention, management and policy of multimorbidity in their respective jurisdictions, with the ultimate goal to foster cross-national partnerships. Key messages The integration of public health and primary care is crucial to improve both prevention and clinical management of multimorbidity. There is a need for collaborative international partnerships, supported by patient and caregiver involvement in research.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.018 | 0.018 |
| Insufficient payload (model declined to judge) | 0.079 | 0.033 |
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".