Equilibrium in the governance of cross-sectoral policies: how does it translate into practice?
Bibliographic record
Abstract
BACKGROUND: There is growing interest from health researchers in the governance of Health in All Policies (HiAP). Furthermore, the COVID-19 pandemic has re-ignited managers' interest in HiAP governance and in health prevention activities that involve actors from outside health ministries. Since the dynamics of these multi-actor, multi-sectoral policies are complex, the use of systems theory is a promising avenue toward understanding and improving HiAP governance. We focus on the concept of equilibrium within systems theory, especially as it points to the need to strike a balance between actors that goes beyond synergies or mimicry-a balance that is essential to HiAP governance. METHOD: We mobilized two sources of data to understand how the concept of equilibrium applies to HiAP governance. First, we reviewed the literature on existing frameworks for collaborative governance, both in general and for HiAP specifically, in order to extract equilibrium-related elements. Second, we conducted an in-depth case study over three years of an HiAP implemented in Quebec, Canada. RESULTS: In total, we identified 12 equilibrium-related elements relevant to HiAP governance and related to knowledge, actors, learning, mindsets, sustainability, principles, coordination, funding and roles. The equilibria were both operational and conceptual in nature. CONCLUSIONS: We conclude that policy makers and policy implementers could benefit from mobilizing these 12 equilibrium-related elements to enhance HiAP governance. Evaluators of HiAP may also want to consider and integrate them into their governance assessments.
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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 | 0.080 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.008 | 0.047 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".