Notice bibliographique
Résumé
IntroductionIn this workshop we will share lessons learned from Canada, England and the Netherlands on how innovations or good practices in integrated care can be widely spread and implemented. We will depict how adopters of innovations can be supported in capacity building. The success of system-level spread is contingent on a large number of well supported and committed adopters.BackgroundOften, it is assumed that national/regional policy measures and structures, such as legislation, funding and types of organisation, explain successful or failing implementation of proven innovations. However, Horton et al. (2018) made a compelling argument that strategies to support the spread and scale of complex innovations should respect and support the role of the adopters. Integrated health and social care programs are social, context sensitive and dynamic. Therefore, implementing complex innovations in other settings requires these innovations to be described in a way that is useful to the intended adopters, as well as paying attention to commitment and capacity building of these adopters. We analysed three national implementation programs according to the principles articulated by Horton and colleagues. In each country, various ways to actively engage and support large numbers of adopting organisations were deployed to make innovations work in their context. Capacity building and encouraging commitment in adopters appeared to be crucial. Based on this analysis, we argue that removing policy barriers and establishing incentives is necessary, but that supporting and committing adopters is another crucial element of spread.Aims and objectivesIn this workshop we actively exchange lessons learned of the three programmes and of the participants of the workshop in terms of key mechanisms for large scale implementation of innovations in integrated care. We aim at learning about scaling up and spreading innovations or good practices across different settings. Target audiencePolicy makers, managers, support staff and commissioners at national, local or organisational level, as well as health management researchers.Learnings/Take awayWe will share how in practice large scale implementation and mutual learning can be organised. The lessons will be transferable to participants’ specific contexts.FormatWe will depict an analytical working model to describe the elements of successful spread (10 minutes). In short presentations, we will demonstrate how the principles of this model were applied in practice in three national programs (aiming at better care closer to home and community, quality and efficiency in integrated long-term care and vanguards in integrated care) (25 minutes). We will share experiences of participants on local implementations, thereby aiming to complement and validate identified mechanisms and translate them to practical guidelines for everyday practice. A checklist will be provided as a helpful tool (40 minutes). Finally, the experiences and outcomes will be briefly discussed in the plenary (15 minutes).Preferred length90 minutesReferencesHorton TJ, Illingworth JH, Warburton WHP. Overcoming Challenges In Codifying And Replicating Complex Health Care Interventions. Health Aff (Millwood). 2018 Feb;37(2):191-197. doi: 10.1377/hlthaff.2017.1161. Available from: https://www.healthaffairs.org/doi/full/10.1377/hlthaff.2017.1161
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 | 0,042 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,008 |
| Communication savante | 0,010 | 0,008 |
| Science ouverte | 0,006 | 0,028 |
| Intégrité de la recherche | 0,005 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,025 | 0,002 |
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 ».