Unemployment, SNAP Enrollment, and Food Insecurity Before and After California's COVID-19 Shutdown
Bibliographic record
Abstract
OBJECTIVES: To examine whether the decrease in very low food security (VLFS) observed in California shortly after California's coronavirus disease (COVID-19) shutdown remained throughout Federal Fiscal Year (FFY) 2020. To investigate associations among unemployment, Supplemental Nutrition Assistance Program (SNAP) enrollment, and VLFS across FFY 2020. METHODS: Telephone interview responses from mothers from randomly sampled households from low-income areas throughout California to the 6-item US Department of Agriculture Food Security Survey Module identified VLFS families. Logistic regression examined VLFS rates before vs after California's COVID-19 shutdown, with race/ethnicity, age, and education as covariates. Pearson correlations were calculated for unemployment, SNAP enrollment, and VLFS. RESULTS: Most (66.4%) of the 2,682 mothers were Latina. VLFS declined from 19.3% before to 14.5% after California's COVID-19 shutdown (adjusted odds ratio, 0.705; P = 0.002). The correlation for unemployment and SNAP household participation was 0.854 (P = 0.007), and for SNAP participation and VLFS was -0.869 (P = 0.005). CONCLUSIONS AND IMPLICATIONS: Publicly-funded assistance programs may lower food insecurity, even during a time of increased economic hardship. Examining the specific factors responsible for the observed decline in VLFS has merit. Whether VLFS remains below the rate observed before California's COVID-19 shutdown is worthy of ongoing study.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".