Governance, Health Policy Implementation and the Added Value of Regionalization
Bibliographic record
Abstract
OBJECTIVES: In this paper we focus on governance and the added value of regionalization in the context of health policy implementation. What are regional boards' patterns of action in the governance process?How do these patterns favour policy implementation? ANALYTICAL FRAMEWORK: To enhance our understanding of the role of regional boards in governance processes, we relied on four conceptual constructs that corresponded to models of collective action: political, technocratic, democratic and cognitive. Alongside the four models, we analyzed the impact of governance on health policy implementation using Mazmanian and Sabatier's general analytical framework, which identifies three types of variables that affect public policy implementation: (1) variables related to the complexity of the problem, (2) statutory variables that structure the implementation of the policy and (3) non-statutory variables related to the context. METHODS: We conducted a qualitative, longitudinal case study of the regional implemention of the Program to Combat Cancer in Quebec. FINDINGS: This research stresses the added value of a clinico-administrative governance of change, whereby regional boards, in synergy with clinical leaders, participate in the orientation of collective action. Analysis of the regional board's patterns of action reveals the utility of combined technocratic, democratic, political and cognitive actions.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".