Journey Through the Fractalization of Multilevel Governance: Levers for Adapt Healthcare Organizations Toward Migrant Populations in Canada
Bibliographic record
Abstract
This article focuses on multilevel governance applied to health organizations in Québec (Canada). The objective is to understand the action levers that facilitate the adaptation of the services toward migrant populations. This type of population establishes itself as an excellent tracer case to analyze the adaptation process, its fractalization and its involvement with the Environment. The dynamics between the actors and their self-organization takes part in the development of a multilevel governance. Interactions with the Environment-both internal and external-highlight the development of networks that emerge from the field and are then implemented at strategic levels in the organizations. The presence of connectivity actors within the organization and the Environment is established. The context, the bonds of trust between the actors and the credibility of the policymakers are reflected as important factors. However, connectivity actors cannot be successful without the support and contribution of the more "hierarchical" actors. Eight action levers are revealed by the analysis. We categorized them in 3 functions: administrative, enabling, and emerging. The levers of the administrative and emerging functions require that the levers of the enabling function be credible and legitimate and be able to support them for the adaptation to spread throughout the healthcare organization, regardless of the scope or policymaking level. The fractal function facilitates this process, by combining connectivity actors with the implementation of connectivity structures.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".