Food Insecure Individuals Are More Prone to Health Problems
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 health issues. In addition, FI also affects psychological well‐being. This study investigated differences in self‐reported health between food secure (fs) and food insecure(fi) individuals in 42 countries through data from the 2014 Gallup World Poll (GWP) with the Food Insecurity Experience Scale (FIES). One question regarding the presence of health problems was selected from the GWP questionnaire to assess respondents' health status. Results showed a prevalence of 61% FI, while 27.2% of interviewees reported health problems that prevented them from engaging in normal activities for a person of their age. In a multivariate logistic model, when compared to fs individuals, those classified as fi showed significantly higher odds (OR) of reporting that they had health problems (OR=1.72, 95% CI: 1.58, 1.87). The same trend was observed for women when compared to men (OR=1.31, 95% CI: 1.23, 1.39), for single individuals when compared to those in a couple (OR=1.17, 95% CI: 1.10, 1.25), for those unable to afford adequate shelter throughout the year (OR = 1.55, 95% CI: 1.45, 1.67) and for those unsatisfied with the healthcare service (OR=1.09, 95% CI: 1.02, 1.17). The statistical model incorporated all variables mentioned above in addition to income, education, household size, age, country and area of residence. These results reveal increased vulnerability to health problems among specific groups and provide evidence of the importance of FS for physical well‐being.
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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 | 0.001 |
| 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".