Focusing attention on the important association between food insecurity and psychological distress: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Food insecurity has involved more than 750 million individuals worldwide. The association of food insecurity with socio-economic factors is also undeniable demand more consideration. Food insecurity will become a global priority by 2030. This systematic review and meta-analysis examined current literature concerning the association between food insecurity and psychological distress. METHODS: Relevant researches were identified by searching databases including PubMed, EMBASE, Scopus, and Web of Science, ProQuest, and Cochrane Library up to June 2024 without language limitation. Then a snowball search was conducted in the eligible studies. The quality assessment was made through Newcastle-Ottawa Scale. RESULTS: Data were available from 44 cross-sectional articles for systematic review and 17 eligible articles for meta-analysis with 2,267,012 and 1,953,636 participants, respectively. Findings support the growing segment of literature on the association between food insecurity and psychological distress. The highly represented groups were households with low income. Psychological and diabetic distress was directly associated with food insecurity as it increased the odds of distress to 329% (OR: 3.29; 95% CI: 2.46-4.40). Sleep problems, anxiety, depression, lower life satisfaction, obesity, and a higher rate of smoking were among the secondary outcomes. CONCLUSION: Food insecurity was a common stressor that can have a negative impact on psychological well-being and even physical health. The findings should be considered in the public health and making policy-making process.
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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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.036 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".