Building bridges to integrate care
Notice bibliographique
Résumé
The widespread epidemiological shift from acute to chronic illnesses in the population has been accompanied by an increasing recognition of the need to simultaneously address the interacting physical, mental health and social needs of patients. A fragmented health care system built around single diseases and institutions cannot effectively address these complex needs. Health system reform is necessary to achieve this but is challenged by a lack of evidence on how to effectively restructure the system and care for these complex patients. Funded by the Ontario Ministry of Health and Long-Term Care (MOHLTC) in Canada, the Building Bridges to Integrate Care (BRIDGES) program at the University of Toronto is jointly led by the Departments of Medicine, Psychiatry and Family and Community Medicine. Led by academics, BRIDGES partners with both the government and providers to address this evidence gap by generating local knowledge on ways in which primary, specialty, hospital and community care may be integrated for patients with complex physical, mental health and social needs. The collaboration with academics supports project teams in developing models that incorporate the best available evidence, engaging in model refinement activities, and rigorously applying qualitative and quantitative evaluation methods. The partnership between academics and government lends credibility to the findings and provides a mechanism through which information from project teams may be consolidated and disseminated to the government to influence work on health system structure and policy reform. To date, nine models of integrated care delivery have partnered with BRIDGES to form a collaborative that has a common focus, adopts similar evaluation approaches, and shares important lessons. Work with these models has highlighted the difficulties of generating and implementing evidence on care integration. Challenges are present in each stage of model design, implementation, improvement, evaluation, scale and dissemination and are reflected in the form of structural, policy, resource and cultural barriers. In the absence of widespread evidence on how to overcome each of these barriers, the BRIDGES model also provides a knowledge translation platform through which teams interact, share learnings and develop a community of practice where health professionals, researchers and government stakeholders learn from each other’s experiences and work together towards an improved, integrated health care system. Ongoing work is needed to identify effective models of care for those with complex medical and social needs. BRIDGES is one approach to filling this knowledge gap and generating evidence on effective care integration for this population.
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 | 0,075 | 0,089 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,011 | 0,013 |
| Communication savante | 0,018 | 0,030 |
| Science ouverte | 0,008 | 0,053 |
| Intégrité de la recherche | 0,010 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,023 | 0,006 |
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 ».