High Prevalence of Food Insecurity and Related Disparities Among US College and University Students From 2015–2019
Bibliographic record
Abstract
OBJECTIVE: We examined food insecurity prevalence among college students included as part of a large, ongoing, nationally representative survey and examined trends and associations with sociodemographic measures. METHODS: Data come from the Panel Study of Income Dynamics, a nationally representative longitudinal household panel survey, and include 2,538 college students from 2015-2019. Food security status was assessed using the US Department of Agriculture's 18-item Household Food Security Survey Module. RESULTS: From 2015 to 2019, 11% of college students experienced marginal food security, and 15% experienced food insecurity. Food insecurity was 12% in 2015 and 14% in 2017 and 2019. More Black and Hispanic students experienced food insecurity than White students (21% and 26%, vs 9%, respectively; P <0.001), as did first-generation than non-first-generation students (18% vs 10%; P = 0.01). CONCLUSIONS AND IMPLICATIONS: College food insecurity is an urgent public health issue demanding greater response from colleges and universities and state and federal governments.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".