Collaborative governance in integrated healthcare networks: insights from the cancer program in Québec (Canada)
Notice bibliographique
Résumé
Context: The challenge of modernizing health services delivery for cancer patients and their loved ones involves moving towards integrated network-based practices. While initiatives at different levels of healthcare systems contribute to this movement, further efforts are needed to maintain and extend gains to adapt to the complex and fast-changing cancer environment. A collaborative governance regime, understood as a form of capacity building for joint action across boundaries, represents a promising way to improve and sustain integrated care. Objective: This presentation reports insights from providers and users of health services gained from the experience in operationalizing an integrated network-of-networks in the context of a national cancer program in Quebec (Canada). Methods: Methods: Insights are drawn from a larger multiple case study that took place from 2018 to 2021. Participants (n=39) are knowledgeable informants on how “mandated” integration structures and processes support collaboration among the various components of the network. Documents (n=45) related to network governance and integrated practices are also included. The data collection guide and analysis based on qualitative interpretive description are framed on the Collaborative Governance framework to capture critical dimensions in the integration process. These include clear mission, common and shared definition of complex problems and potential solutions, and identification of common values to support motivation, engagement and capacity for joint action. Results: We report on three main tensions related to integration in the cancer network that suggest strategies for enhancing collaborative governance: The first tension emerges as some participants perceive “mandated integration” within a “prescribed collaborative work” as a constraint that undermines previous collaborative work at local level. This tension leads to unresolved controversies within and between actor groups, weakening the mechanisms of collaborative governance. The second tension is a principle-to-practice divide around engaging people affected by cancer in network governance. Despite widespread motivation, participation at the local level varies according to managers' willingness and skill in creating open and inclusive processes where evidence-based medical practice and lived patient experience can be assembled. This tension is seen to contribute to a poor consideration of patient perspectives during the highly volatile period of decision-making during the COVID-19 pandemic. The third tension involves the hierarchy of cancer services promoted in the national cancer program and the standardization of practices. Organizing the network-of- networks by cancer specialization involves creating trajectories by type of cancer and their navigation according to established standards of practice and quality considerations. The consequent redistribution of human and financial resources, and revised ownership of care process creates perceived asymmetries between cancer network professionals and centers and controversies that hinder integrated practices. Conclusion: Despite favorable starting conditions from the national cancer program and its central leadership promoting collaborative governance, tensions that emerge through the pursuit of network integration limit the transition to a more collaborative regime. Taking the time to work out these tensions as integration proceeds appears essential to arrive at a governance model that is appropriate and acceptable for all network members.
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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 | 0,004 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,024 | 0,008 |
| Communication savante | 0,006 | 0,002 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
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 ».