Characteristics of injuries among Syrian refugees in Lebanon
Bibliographic record
Abstract
Introduction Injuries sustained before and during displacement contribute significantly to the health burden of refugees, a challenge compounded by limited access to care in host settings. This study aimed to assess the prevalence, causes, outcomes and impacts of physical injuries among Syrian refugees residing in informal settlements across Lebanon. Methods A cross-sectional, cluster-sampling survey was conducted among Syrian refugees in informal settlements across three regions in Lebanon: North, Central-Beirut and South. The Surgeons Overseas Assessment of Surgical Need tool was used to collect data on demographics, injuries sustained in the prior 12 months and associated outcomes. Descriptive and inferential statistics, including χ 2 tests, were used to identify relationships between injuries and household variables. Results Among 1468 surveyed households, 3% reported a death in the past 12 months, 15% of which were injury-related. Nearly 19% of households reported at least one injury. Injuries were more common among males (78.1%) and most frequently involved the extremities. Almost 30% of injured individuals experienced reduced ability to work. Injury occurrence was significantly associated with household type, region and size (p<0.01). Falls were most frequent in the Central region (43%), while motor vehicle crashes (MVCs) dominated in the South (42%). Falls were common in children aged 1–10 years and adolescents, whereas MVCs were more prevalent in adolescents and young adults. Conclusion Syrian refugees in Lebanon face a high injury burden, affecting daily function and livelihoods. Targeted injury prevention interventions in informal settlements are needed to reduce this burden and its long-term consequences.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".