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Record W4310942464 · doi:10.2196/41144

Health System Resilience in the Eastern Mediterranean Region: Perspective on the Recent Lessons Learned

2022· article· en· W4310942464 on OpenAlexvenueno aff
Mirwais Amiri, Mohannad Al Nsour, Alvaro Alonso‐Garbayo, Abdulwahed Al Serouri, Adna Maiteh, Elsheikh Badr

Bibliographic record

VenueInteractive Journal of Medical Research · 2022
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsnot available
FundersWorld Health Organization
KeywordsPublic healthPublic relationsPolitical sciencePsychological resilienceResilience (materials science)WorkforceVulnerability (computing)Economic growthMedicinePsychologyNursing

Abstract

fetched live from OpenAlex

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.

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)0.041
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0410.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.008
Insufficient payload (model declined to judge)0.0010.000

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 head0.405
GPT teacher head0.604
Teacher spread0.199 · 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; both teacher heads agree on what is shown here.

Study designQualitative
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

Citations4
Published2022
Admission routes1
Has abstractyes

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