Why engagement matters in policy implementation: an examination of the engagement of policy actors in the implementation of a mental health and addictions strategy
Notice bibliographique
Résumé
Shifts toward “new public governance” (NPG), where policy decisions and their implementation are “co-produced” by a policy network, have captured the attention of policymakers as an approach that may produce better outcomes. It is particularly promising in mental health, where it is increasingly acknowledged that effective change requires actions by multiple actors across a range of policy and system settings. In Ontario, Canada, the government’s recent mental health reform, Open Minds, Healthy Minds, Ontario’s Comprehensive Mental Health and Addictions Strategy, is unique in terms of its scope and the NPG-inspired processes employed. This study addresses two questions: 1) Who was engaged in the implementation of the Strategy and how were they engaged? and 2) How and why did their involvement contribute to the implementation process and early outcomes? We used a single case study design and partnered with the Ontario Ministry of Health. We used two complementary analytical methods: 1) stakeholder analysis, and 2) political landscape analysis. Seventeen interviews were conducted with citizens, government officials and people with lived experience, and 21 documents analyzed using directed content analysis and drawing from theoretical frameworks regarding political and actor-related determinants of implementation. Stakeholder analysis highlighted the wide range of interdependent actors involved, the multiple ways they provided input, and the structures utilized. The political landscape analysis revealed the role of interests as having a large influence on the implementation process and early outcomes, particularly political actors’ decision to tie the process to their election platform. Relational and contextual variables, such as the relative instability of the policy actors, had a negative impact on the process, but that was offset by the perceived level of dedication of the individuals involved. Our findings point to five practical insights for policymakers and implementers: being attentive to the power distribution among actors, the importance of building and maintaining trust amongst actors, the opportunity-cost of taking a NPG approach, the timing/sequencing of the process, and the need for careful consider of the type of actors involved and the expertise they bring. • This study helps bridge the empirical gap between implementation science and public management by applying an interdisciplinary approach to policy implementation. • Using rigorous qualitative methods, we examined the role of non-governmental actor engagement in policy implementation, contributing to understanding of co-design and new public governance. • Our comprehensive analysis, including stakeholder mapping, analysis of the political landscape and analysis of policy actor determinants of implementation demonstrate how multiple analyses covering distinct domains can provide a more fulsome picture of policy implementation. • We offer five practical insights for policymakers and implementers for improving stakeholder engagement in evidence-informed policy implementation in health and social systems.
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 | 0,086 | 0,095 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,005 |
| Études des sciences et des technologies | 0,030 | 0,034 |
| Communication savante | 0,020 | 0,014 |
| Science ouverte | 0,003 | 0,014 |
| Intégrité de la recherche | 0,007 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».