Why engagement matters in policy implementation: an examination of the engagement of policy actors in the implementation of a mental health and addictions strategy
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.086 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.030 | 0.034 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".