The Impact of Nutrition Knowledge and Perceived Stress on Food Insecurity Among Syrian Refugees in Florida (P04-079-19)
Bibliographic record
Abstract
Food insecurity, nutrition knowledge, and perceived stress were assessed among Syrian refugees in urban and/or rural areas in Florida. The relationship between food insecurity and nutrition knowledge was determined as well. A comprehensive 228-question questionnaire was administered to 80 households (n = 80, 43 in rural area, 37 in urban area). Families with children and couple families with children and additional individuals living in the same household were interviewed (87.5% families with children, 12.5% families with children and additional individuals). Interviewees included women and men (63 women, 17 men) of households with duration of stay in US of >24 months (mean ± SD) (26.4 ± 6.8). Food insecurity scale showed that refugees in urban and rural areas are moderately food insecure without hunger (4.5 ± 2.8, 4.9 ± 2.4 respectively). The mean of nutrition knowledge score was 42.0 ± 13.6 among all of the refugees; refugees in urban scored 41.5 ± 10.9 and in rural areas was 42.7 ± 16.3. It was estimated that Syrian refugees have fair nutrition knowledge. There might be a positive but not significant correlation between food insecurity and nutrition knowledge (r = 0.07 and P > 0.05). Perceived stress scale (20.9 ± 9.0) indicated a low level of stress. However, refugees residing in urban areas had moderate perceived stress scale (24.6 ± 6.1) compared to refugees residing in rural areas (17.8 ± 9.1). A significant (P < 0.01) correlation was observed between low perceived stress and food insecurity among refugees in rural areas (r = 0.38, P = 0.01). There was a positive insignificant correlation between perceived stress and food insecurity among refugees in urban areas (r = 0.1, P > 0.05). Syrian refugees have fair nutrition knowledge. Refugees in urban areas experience greater perceived stress compared to refugees in rural areas. Low power might be contributed to our findings. Increasing sample size may be recommended. N/A.
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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 | 0.000 |
| 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".