11.D. Workshop: Global Burden of Multimorbidity: from Epidemiology to Policy
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,011 | 0,004 |
| Science ouverte | 0,004 | 0,008 |
| Intégrité de la recherche | 0,018 | 0,018 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,079 | 0,033 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».