Multi-sectoral collaborations in selected countries of the Eastern Mediterranean region: assessment, enablers and missed opportunities from the COVID-19 pandemic response
Notice bibliographique
Résumé
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has emphasized the importance of multi-sectoral collaboration to respond effectively to public health emergencies. This study aims to generate evidence on the extent to which multi-sectoral collaborations have been employed in the macro-level responses to the COVID-19 pandemic in nine selected countries of the Eastern Mediterranean region (EMR). METHODS: The study employed in-depth analytical research design and was conducted in two phases. In the first phase, data were collected using a comprehensive documentation review. In the second phase, key informant interviews were conducted to validate findings from the first phase and gain additional insights into key barriers and facilitators. We analysed the macro-level pandemic responses across the following seven components of the analytical framework for multi-sectoral collaborations: (1) context and trigger; (2) leadership, institutional mechanisms and processes; (3) actors; (4) administration, funding and evaluation; (5) degree of multi-sectoral engagement; (6) impact; and (7) enabling factors. RESULTS: Governments in the EMR have responded differently to the pandemic, with variations in reaction speed and strictness of implementation. While inter-ministerial committees were identified as the primary mechanism through which multi-sectoral action was established and implemented in the selected countries, there was a lack of clarity on how they functioned, particularly regarding the closeness of the cooperation and the working methods. Coordination structures lacked a clear mandate, joint costed action plan, sufficient resources and regular reporting on commitments. Furthermore, there was no evidence of robust communication planning both internally, focused on promoting internal consensual decision-making and managing power dynamics, and externally, concerning communication with the public. Across the selected countries, there was strong representation of different ministries in the pandemic response. Conversely, the contribution of non-state actors, including non-governmental organizations, civil society organizations, the private sector, the media and citizens, was relatively modest. Their involvement was more ad hoc, fragmented and largely self-initiated, particularly within the selected middle- and low income- countries of the EMR. Moreover, none of the countries incorporated explicit accountability framework or included anti-corruption and counter-fraud measures as integral components of their multi-sectoral plans and coordination mechanisms. Key enablers for the adoption of multi-sectoral collaborations have been identified, paving the way for more efficient responses in the future. DISCUSSION: Mirroring global efforts, this study demonstrates that the selected countries in the EMR are making efforts to integrate multi-sectoral action into their pandemic responses. Nevertheless, persistent challenges and gaps remain, presenting untapped opportunities that governments can leverage to enhance the efficiency of future public health emergency responses.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,024 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,008 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».