Teasing apart “the tangled web” of influence of policy dialogues: lessons from a case study of dialogues about healthcare reform options for Canada
Bibliographic record
Abstract
BACKGROUND: The knowledge exchange literature suggests that policy dialogues are intended to enhance short-, medium- and long-term capacities of individuals, organizations and health systems to use evidence to inform policy-making. Key features of effective dialogues have been suggested, but the linkages between these features and the realization of improved capacities for evidence-informed policy-making among dialogue attendees and the subsequent influence on policy-making activities are not well understood. METHODS: We conducted a qualitative case study of a series of four policy dialogues that were convened in Canada among national, provincial and regional stakeholders on topics pertaining to healthcare financing and funding in 2011. Data sources included videos of participant perspectives captured during or immediately following each event and follow-up key informant interviews among dialogue participants held 4 years later in 2015. Three conceptual frameworks pertaining to (i) policy dialogues and capacities for evidence use, (ii) factors shaping policy-making across the policy cycle and (iii) factors shaping implementation of evidence guided the thematic analysis. We then synthesized the findings across the three frameworks. RESULTS: The results suggest the potential benefits of policy dialogues described in the literature were developed among the participants at these dialogues. Informants elaborated on how dialogue features influenced their capacities to use evidence, the ideas, interests and institutions during the agenda-setting and policy formulation stages of policy-making and how implementation was affected by characteristics of policy options, individuals, organizations, the external environment and processes. CONCLUSIONS: We present a conceptual framework that furthers our understanding of the potential influence of policy dialogues on the content and mechanisms of policy development and illustrate pathways of influence on various stages of the policy cycle from agenda setting through formulation and implementation. The framework highlights important factors for consideration in designing and evaluating policy dialogues and in supporting post-dialogue knowledge exchange efforts.
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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 | 0.049 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.068 | 0.048 |
| Scholarly communication | 0.025 | 0.015 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".