Food insecurity, state fragility and youth mental health: A global perspective
Bibliographic record
Abstract
Youth in fragile settings face disproportionate risks of experiencing food insecurity and poor mental health. Cross-national evidence is lacking on the association between food insecurity and mental health in youth populations, and on state fragility as a social determinant of these experiences. We analysed data from six cycles of the Gallup World Poll (2014–2019), an annual survey that contains multi-item scales of food insecurity, mental health problems and positive wellbeing. The analytic sample included 164,118 youth aged 15–24 years in 160 states. We linked individual responses to state-level data from the Fragile States Index—an aggregate measure of state vulnerability to collapse or conflict (coded: sustainable, stable, warning, or alert) and estimated adjusted relative risk (RR) of food insecurity as a function of state fragility. We then used linear regression to examine associations of state fragility and food insecurity with mental health and wellbeing. The prevalence of moderate or severe food insecurity rose from 22.93% in 2014 to 37.34% in 2019. State fragility (alert vs. sustainable) was related to an increased risk of food insecurity (RR = 2.28 [95% CI 1.30 to 4.01]), more mental health symptoms (b = 6.36 [95% CI 1.79 to 10.93]), and lower wellbeing (b = −4.49 [95% CI -8.28 to −0.70]) after controlling for state wealth and household income. Increased food insecurity (severe vs. none or mild) was uniquely related to more mental health symptoms (b = 18.44 [95% CI 17.24 to 19.64]) and reduced wellbeing (b = −9.85 [95% CI -10.88 to −8.83]) after state fragility was also controlled. Globally, youth experience better mental health where states are more robust and food access is more secure. The findings underscore the importance of strong governance and coordinated policy actions that may improve youth mental health.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".