Food Insecure Women with Lower Education Report More Health Problems in a Global Sample of Individuals
Bibliographic record
Abstract
Because of its intimate relationship with an insufficient dietary intake of nutrients essential for a healthy and active life, food insecurity (FI) is associated with a wide range of physical and mental health issues. Education is linked with better health through multiple pathways, including job prospects and income, risk for disease, intra‐generational effects, and behavioral and social factors. In most societies, especially in developing countries, women are more vulnerable than men to FI and to poor education levels. The present research uses the Food Insecurity Experience Scale (FIES) in the Gallup World Poll (GWP) to examine the impact of FI and education on self‐reported health status across nationally representative samples from 140 countries. This study aimed to ascertain the effects of FI, gender, and education on the likelihood that participants report health problems (HP). It also explored potential interactions in their effect on health. Data from the 2014 GWP were analyzed using IBM SPSS 21 with the Complex Samples module. One question regarding the presence of HP was selected as the outcome of interest. FI was assessed using the 8‐item FIES, considering food insecure (fi) individuals to be those who answered affirmatively to at least one questionnaire item. Multivariable logistic regression analysis was used to determine the effect of the interaction term of FI, education and gender on HP. The model was adjusted for age, income, water quality, and household size. Data was weighted by country population size. The sample included 136,667 individuals, of which 50.1% were female, 24.9% reported having a HP, and 45.3% were fi. Regarding education, 45.4% completed elementary education or less, 46.1% completed up to three years of tertiary education, and 8.5% completed four years beyond ‘high school’ and/or received a college degree. The interaction term (FI, education, and gender) was statistically significant (p < 0.001). When compared to food secure men with the highest education level, fi women with low education level presented the highest odds of reporting HP (OR=5.2). Results reveal increased vulnerability to HP among women, fi individuals, and those with lower education levels, providing evidence that health is determined not only by internal factors as genetics or external ones such as healthy lifestyle, but also by social factors. These social determinants are important to take into account in health‐related policy decisions and program design.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".