Toward Public Health Resilience in the Eastern Mediterranean Region: Findings From the Seventh Eastern Mediterranean Public Health Network Regional Conference
Bibliographic record
Abstract
The resilience of public health in the Eastern Mediterranean Region (EMR) varies from country to country, mostly based on the governmental and financial situation of the countries. With the theme of Towards Public Health Resilience in the EMR: Breaking Barriers, the seventh Eastern Mediterranean Public Health Network regional conference, held from November 14 to 18, 2021, was dedicated to exploring ways for achieving public health resilience. A total of 101 oral presentations and 13 poster presentations were presented on various public health topics. The conference included 6 keynote sessions, 10 roundtable sessions, and 5 preconference workshops. The preconference workshops were conducted on border health; the mobilization of Field Epidemiology Training Program (FETP) residents and graduates and rapid responders in EMR countries; continuous professional development for the public health workforce; brucellosis surveillance using the "One Health" approach; and strategies to integrate and use noncommunicable diseases data sources. The roundtable sessions included discussions on the following topics: the role of FETPs in responding to COVID-19, institutionalization of rapid response to public health emergencies, health systems resilience, integration of early warning and response with event-based and indicator-based surveillance, sustaining international health regulations, strengthening the "One Health" approach, the anticipated future of public health in the post COVID-19 era, supporting public health research capacity in a diverse region, and COVID-19 vaccines and routine immunization synergies and drawbacks. The keynote speaker sessions covered topics on essential public health functions and the universal health coverage challenge in the EMR, lessons from the US COVID-19 public health response, learning from COVID-19, reshaping public health after the pandemic era, COVID-19 resilient primary health care, and the cohesion of society during and after a pandemic. The conference sessions provided highly promising opportunities to explore ways to achieve such goals in the EMR and shed light on the latest scientific findings, important lessons learned, and discussions on the ways in which current barriers can be broken down through coordination and collaboration.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".