Suburban and Rural Women: Internationally the most Vulnerable to Food Insecurity
Bibliographic record
Abstract
People in rural (Ru) and impoverished suburban areas (Sb), as well as women, have higher rates of food insecurity (FI) when compared to other populations. This has been reported when applying different FI measurement tools in diverse settings. In this study FI was assessed using the Food Insecurity Experience Scale (FIES) as part of the 2014 Gallup World Poll (GWP). The objective was to determine differences in FI by gender and area of residence using the same measurement tool (FIES) as applied by GWP in individuals of 42 countries in diverse settings. Differential Item Functioning (DIF) was previously assessed using Rasch model. No significant DIF for any of the 8 items in FIES was detected between men and women (DIF 蠄 0.25 logit), or between individuals in four settings (Ru, Sb, small towns (St) and large cities (Lc)) (DIF 蠄 0.40 logit). Men living in Lc showed the lowest prevalence of FI when compared to men and women living in the other settings. Multivariate logistic regression analysis showed that women of Sb, Ru, St, and Lc had significantly higher odds ratios for FI (OR: 1.74, 1.70, 1.28, 1.14, respectively) when compared to men in Lc. Additionally, men in Sb, Ru and St, had also significantly higher ORs for FI (1.57, 1.47, 1.28, respectively) when compared to their male peers in Lc. This analysis was adjusted by country of residence, level of education, income, age, household size and marital status. The results highlight the importance of developing and carrying out interventions that incorporate the challenges faced by individuals belonging to diverse populations. Global gender disparities related to FI must be considered by policy makers and practitioners in order to more effectively address this phenomenon.
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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) | 0.007 | 0.001 |
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".