Gouvernance territoriale et performance des services de santé: perspectives pour les Groupements Sanitaires Territoriaux au Maroc
Bibliographic record
Abstract
Cet article analyse les conditions de réussite des Groupements Sanitaires Territoriaux (GST) au Maroc, une innovation visant à décentraliser et intégrer les services de santé. S’appuyant sur le management public, les théories de la gouvernance et le cadre du Triple Aim, l’étude explore les leviers d’une gouvernance territoriale performante. La méthodologie repose sur une revue de littérature, une analyse comparative des réformes au Canada, en Espagne et en France, ainsi que sur l’examen des textes législatifs marocains (BO 7151, 7213) et des rapports institutionnels (OMS, HCP). Les résultats identifient comme facteurs clés : une territorialisation basée sur les besoins populationnels, une autonomie locale effective, des outils de pilotage (tableaux de bord, contractualisation) et un renforcement des capacités managériales, malgré des défis liés à la centralisation et aux disparités régionales. L’article préconise une gouvernance multi-niveaux agile, favorisant coordination, responsabilisation et expérimentation. Des recherches empiriques, notamment sur le pilote de Tanger-Tétouan-Al Hoceima, sont nécessaires pour évaluer l’impact des GST sur l’équité et la performance des services de santé. This article examines the conditions for the success of Territorial Healthcare Groups (GSTs) in Morocco, an innovative initiative aimed at decentralizing and integrating healthcare services. Drawing on public management, governance theories, and the Triple Aim framework, the study explores the levers for effective territorial governance. The methodology combines a literature review, a comparative analysis of healthcare reforms in Canada, Spain, and France, and an examination of Moroccan legislative texts (BO 7151, 7213) and institutional reports (WHO, HCP). The findings highlight key success factors: territorialization based on population needs, effective local autonomy, management tools such as dashboards and contractual agreements, and strengthened managerial capacities, despite challenges posed by historical centralization and regional disparities. The article advocates for agile, multi-level governance that promotes coordination, accountability, and experimentation. Further empirical research, particularly on the Tanger-Tetouan-Al Hoceima pilot, is essential to assess the GSTs’ impact on equity and healthcare service performance.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".