Community-driven network building in health care: creating an exploratory social space to pursue co-production following reforms
Notice bibliographique
Résumé
Community-driven network building in health care: creating an exploratory social space to pursue co-production following reformsIntroduction:Integrated care requires engaging and empowering people and communities to take an active role in designing and delivering health services (WHO 2019). Their capacities to co-produce health outcomes alongside formal providers are key to assuring sustainability and equity. However collaborative dynamics are compromised by power differentials that limit recognition of community capacities, and are vulnerable to system reforms. Much of the literature on co-production focuses on provider-side initiatives to engage communities. This paper provides an alternate view, exploring how community actors forge network alliances to gain legitimacy and power to co-produce care.Methods:We conducted a longitudinal case study of the network development efforts of a community working group (WG) concerned with access to health services following reforms in Québec, Canada. The WG brought together concerned citizens along with community organizations working with seniors, minorities, immigrants, youth, and people with disabilities. Data were collected over three years from observation, documents and interviews, and social network analysis was conducted to reveal the evolution of relationships among community actors, and between community and public actors. Actor-network theory (Callon) was used to distinguish stages of network maturation. These analyses explored how interactions contributed to identifying and opening pathways for co-production.Results:The WG pursued network building in two stages. A first focused on problem definition: WG members brought their existing networks together to validate access problems perceived in their constituencies, then reached out as a group to public sector contacts to achieve a better understanding of precisely what had changed in the system. In a second stage, the WG mobilized this network of community and public actors to equip a broader public to more effectively draw upon public and community resources to meet their needs. Two factors appeared to impede co-production: the limited influence of front-line actors on public system processes; and discrepancies between community priorities and system mechanisms for participation.Discussion:In the context of reforms, 'problematization' was an especially important stage in network development and showed signs of consensus development on 1. the existence and nature of problems, and 2. interdependencies between public and community actors in identifying and implementing solutions.Conclusions:Network development through the WG enabled community actors to gain the "organizational infrastructure" to participate in collaborative governance (Ansell and Gash 2008). Community efforts can open new spaces to enhance co-productive capacities of people and communities; public provider ability to integrate these capacities into processes is reduced by reforms.Lessons:Network interactions enable the recognition of interdependencies and the development of consensus, and in this way create conditions for collaboration even among actors of different strengths (Benson).The fragility and disruption through reforms of links between public and community actors impede co-production.LimitationsLonger follow-up and comparison with other community initiatives may have provided additional insight into community strategies for gaining legitimacy in co-production.Future researchResearch on factors limiting the effectiveness of longstanding 'concertation' venues after reforms would be helpful, as would exploration of territorial dimensions of co-production between public and community actors in healthcare services.
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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 | 0,024 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,020 | 0,028 |
| Communication savante | 0,012 | 0,011 |
| Science ouverte | 0,004 | 0,023 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
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 ».