Multi-sectoral collaborations in selected countries of the Eastern Mediterranean region: assessment, enablers and missed opportunities from the COVID-19 pandemic response
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".