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Record W4391160842 · doi:10.1186/s12961-023-01098-z

Multi-sectoral collaborations in selected countries of the Eastern Mediterranean region: assessment, enablers and missed opportunities from the COVID-19 pandemic response

2024· article· en· W4391160842 on OpenAlexaff
Fadi El‐Jardali, Racha Fadlallah, Najla Daher

Bibliographic record

VenueHealth Research Policy and Systems · 2024
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsMcMaster UniversityImpact
FundersUnited Nations Development Programme
KeywordsHealth administrationPandemicMandatePublic healthClosenessCLARITYHealth services researchContext (archaeology)DocumentationPolitical scienceAction planPublic relationsHealth informaticsBusinessMedicineCoronavirus disease 2019 (COVID-19)GeographyNursingEconomicsDiseaseInfectious disease (medical specialty)Computer scienceManagement

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.032
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.402
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.890
GPT teacher head0.614
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations38
Published2024
Admission routes1
Has abstractyes

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