Understanding the supports needed for policy implementation: a comparative analysis of the placement of intermediaries across three mental health systems
Bibliographic record
Abstract
BACKGROUND: Intermediaries are organisations or programmes that work between policy-makers and service providers to facilitate effective implementation of evidence-informed policies, programmes and practices. A number of intermediaries now exist in well-established mental health systems; however, research on them, and how they may be optimised to support implementation is lacking. This research seeks to understand the puzzling variation in the system placement of intermediaries supporting policy implementation in the mental health systems of Canada (Ontario), New Zealand and Scotland. METHODS: Using a comparative case study approach, the analytic goal was to compare intermediaries across jurisdictions and explain differences in their placement using explanatory frameworks from political science. Data for this analysis were derived from several sources, including key informant interviews, a literature search of published and grey literature on intermediaries and on policy implementation in mental health systems, a review of relevant policy documents and websites, as well as documents and websites relating to the various intermediaries and other interest groups within each system. RESULTS: Through the analysis, we argue that the placement of intermediaries supporting policy implementation can be explained through an understanding of the political structures, the policy legacies leading to the current public/private mix of mental health service delivery, and the differing administrative capacities of mental health systems. CONCLUSIONS: This research contributes to our growing understanding of policy-related intermediaries supporting implementation at scale and how we might build appropriate infrastructure in systems to support the implementation of policy and achieve better outcomes for citizens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".