Health System Resilience in the Eastern Mediterranean Region: Perspective on the Recent Lessons Learned
Bibliographic record
Abstract
BACKGROUND: Public health has a pivotal role in strengthening resilience at individual, community, and system levels as well as building healthy communities. During crises, resilient health systems can effectively adapt in response to evolving situations and reduce vulnerability across and beyond the systems. To engage national, regional, and international public health entities and experts in a discussion of challenges hindering achievement of health system resilience (HSR) in the Eastern Mediterranean Region, the Eastern Mediterranean Public Health Network (EMPHNET) held its seventh regional conference in Amman, Jordan, between November 15 and 18, 2021, under the theme "Towards Resilient Health Systems in the Eastern Mediterranean: Breaking Barriers." This viewpoint paper portrays the roundtable discussion of experts on the core themes of that conference. OBJECTIVE: Our aim was to provide insights on lessons learned from the past and explore new opportunities to attain more resilient health systems to break current barriers. METHODS: The roundtable brought together a panel of public health experts representing Field Epidemiology Training Programs (FETPs), Centers for Disease Control and Prevention in Atlanta, World Health Organization, EMPHNET, universities or academia, and research institutions at regional and global levels. To set the ground, the session began with four 10-12-minute presentations introducing the concept of HSR and its link to workforce development with an overall reflection on the matter and lessons learned through collective experiences. The presentations were followed by an open question and answer session to allow for an interactive debate among panel members and the roundtable audience. RESULTS: The panel discussed challenges faced by health systems and lessons learned in times of the new public health threats to move toward more resilient health systems, overcome current barriers, and explore new opportunities to enhance the HSR. They presented field experiences in building resilient health systems and the role of FETPs with an example from Yemen FETP. Furthermore, they debated the lessons learned from COVID-19 response and how it can reshape our thinking and strategies for approaching HSR. Finally, the panel discussed how health systems can effectively adapt and prosper in the face of challenges and barriers to recover from extreme disruptions while maintaining the core functions of the health systems. CONCLUSIONS: Considering the current situation in the region, there is a need to strengthen both pandemic preparedness and health systems, through investing in essential public health functions including those required for all-hazards emergency risk management. Institutionalized mechanisms for whole-of-society engagement, strengthening primary health care approaches for health security and universal health coverage, as well as promoting enabling environments for research, innovation, and learning should be ensured. Investing in building epidemiological capacity through continuous support to FETPs to work toward strengthening surveillance systems and participating in regional and global efforts in early response to outbreaks is crucial.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".