Households’ Food Insecurity and Its Association with Demographic and Socioeconomic Factors in Gaza Strip, Palestine: A Cross- Sectional Study
Bibliographic record
Abstract
Background: This sudy aimed to identify the prevalence of household's food insecurity and its association with demographic and socioeconomic factors. Methods: A cross-sectional study was conducted in September 2021 among a representative sample of households in the Gaza strip governorates. A total of 1167 households randomly selected from all five governorates and were included in the study. The Radimer/Cornell food security scale was used to determine the prevalence and levels of household food insecurity. The household's demographic and socioeconomic characteristics were obtained using an interview-based questionnaire. Statistical analysis was performed using SPSS version 25. Results: The overall prevalence of household's food insecurity was 71.5%. The prevalence by governorates was highest in Gaza (30.8%), followed by Khanyounis (23.0%), North-Gaza (18.6%), Middle-Area (15.2%) and Rafah (12.4%). Regarding the food insecurity levels, 333 (28.5%) of the households were food secure, 422 (36.2%) had mild food insecurity, 161 (13.8%) had moderate food insecurity, and 251 (21.5%) had severe food insecurity. Significant associations were found between governorates, monthly income, homeownership, work status with the household's food insecurity, (Crude OR [COR] = 2.02, 95% CI = [1.02-3.98], P value < 0.05), (COR = 2.00, 95% CI = [1.04-2.75], P value < 0.05), (COR = 2.36, 95% CI = [1.39-3.99], P value < 0.05), and (COR = 1.14, 95% CI = [0.66-1.97], P value < 0.05), respectively. Conclusions: Our study demonstrates that food insecurity is highly prevalent in the Gaza strip and is associated with poor living conditions. Therefore, this high prevalence should be seriously discussed and urgently considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".