COVID-19 Benefits and Dietary Behaviors Among Mothers From Low-Income, Food-Insecure Households
Bibliographic record
Abstract
OBJECTIVE: To examine the dietary behaviors of mothers from very low food security (VLFS) households following the availability of coronavirus disease 2019 (COVID-19) unemployment and Supplemental Nutrition Assistance Program benefits. METHODS: Diet and food security status were obtained from 2,584 California mothers during Federal Fiscal Year 2020. Fruits, vegetables, and 100% fruit juice (FV100%FJ), sugar-sweetened beverages, and water intake, and Healthy Eating Index-2015 scores, were compared across 4 groups (before vs after COVID-19 benefits by VLFS vs non-VLFS households) with race/ethnicity and age as covariates. RESULTS: Before COVID-19 benefits, VLFS was associated with fewer cups of FV100%FJ (P = 0.010), more fluid ounces of sugar-sweetened beverages (P = 0.004), and poorer diet quality (P = 0.003). After COVID-19 benefits, mothers from VLFS vs non-VLFS households reported similar dietary outcomes. VLFS mothers reported 0.96 (95% confidence interval, 0.53-1.38) more cups of FV100%FJ after COVID-19 benefits. CONCLUSIONS AND IMPLICATIONS: Coronavirus disease 2019 benefits may have reduced dietary inequities among low-income families. Associations between increased Supplemental Nutrition Assistance Program and unemployment benefits and decreased costs associated with the negative health outcomes linked to food insecurity and poor diets would be of value.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".