Differences in Initial Healthcare Responses Between Turkey and the Conflict-Ridden Area in Syria Following February 2023 Earthquakes
Bibliographic record
Abstract
Abstract Objective This study analyzes disparities in initial healthcare responses in Turkey and Syria following 2023 earthquakes. Methods Using Humanitarian Data Exchange, Crude Mortality Rates (CMR) and injury rates in both countries were calculated, and temporal trends of death tolls and injuries in the first month post- catastrophe were compared. WHO Flash Appeal estimated funding requirements, and ratios of humanitarian aid personnel in Urban Search and Rescue (USAR) teams per population from ReliefWeb and MAPACTION data were used to gauge disparities. Results 56,051,096 individuals were exposed, with Turkey having 44 million vs 12 in Syria. Turkey had higher CMR in affected areas (10.5 vs 5.0/10,000), while Syria had higher CMR in intensely seismic regions (9.2 vs 7.7/1,000). Turkey had higher injury rates (24.6 vs 9.9/10,000). Death and injury rates plateaued in Syria after three days, but steadily rose in Turkey. Syria allocated more funding for all priorities per population except healthcare facilities’ rehabilitation. Turkey had 219 USAR teams compared to Syria’s six, with significantly more humanitarian aid personnel (23 vs 2/100,000). Conclusions Significant disparities in initial healthcare response were observed between Turkey and Syria, highlighting need for policymakers to enhance responses in conflict-affected events to reduce impact on affected populations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".