Prevalence and risk factors of food insecurity among Syrian refugees in Türkiye
Bibliographic record
Abstract
BACKGROUND: Although Türkiye (Turkey) hosts the largest number of Syrian refugees, studies on food insecurity are limited. This study examined the prevalence and risk factors of food insecurity among Syrian refugees living in Istanbul, which has the highest number of refugees in Türkiye. METHODS: A cross-sectional survey was conducted among Syrian refugees in Istanbul between September 2021 and March 2022. The main income earners of 103 households were interviewed by a research dietitian, with the assistance of an Arabic speaking interpreter through hour-long face-to-face. Data on sociodemographic characteristics (age, gender, nationality, marital status, educational status, the family income, the major source of family income, and the number of family members living in the household etc.) and household food insecurity status were collected. Household food insecurity status was assessed with the eighteen-item Household Food Security Survey Module. RESULTS: The household food insecurity rate was 90.3%, and those of adults and children were 88.4% and 84.8%, respectively. It was observed that family income level was significantly associated with food insecurity. A one-unit increase in monthly income increased food security by 0.02 times (p < 0.001). The number of employed refugees in the food security group was higher than that in the food insecurity group (p = 0.018). A significant difference was found in the rate of occupation type of the major income earner between the groups (p = 0.046). CONCLUSIONS: High rates of food insecurity, particularly severe food insecurity, were found among Syrian refugees living in Istanbul. While more research is warranted to explore the root causes and efficacy of the current support system, it requires the immediate attention of policymakers at the national and international levels to implement effective policies and interventions.
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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 | 0.001 | 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) | 0.002 | 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".