Regional differences in the impact of the COVID-19 pandemic on food sufficiency in California, April–July 2020: implications for food programmes and policies
Bibliographic record
Abstract
OBJECTIVE: To evaluate regional differences in factors associated with food insufficiency during the initial months of the COVID-19 pandemic among three major metropolitan regions in California, a state with historically low participation rates in the Supplementation Nutrition Assistance Program, the nation's largest food assistance programme. DESIGN: Analysis of cross-sectional data from phase 1 (23 April-21 July 2020) of the US Census Household Pulse Survey, a weekly national online survey. SETTING: California, and three Californian metropolitan statistical areas (MSA), including San Francisco-Oakland-Berkeley, Los Angeles-Long Beach-Anaheim and Riverside-San Bernardino-Ontario MSA. PARTICIPANTS: Adults aged 18 years and older living in households. RESULTS: Among the three metropolitan areas, food insufficiency rates were lowest in the San Francisco-Oakland-Berkeley MSA. Measures of disadvantage (e.g., having low-income, being unemployed, recent loss of employment income and pre-pandemic food insufficiency) were widely associated with household food insufficiency. However, disadvantaged households in the San Francisco Bay Area, the area with the lowest poverty and unemployment rates, were more likely to be food insufficient compared with those in the Los Angeles-Long Beach-Anaheim and Riverside-San Bernardino-Ontario MSA. CONCLUSIONS: Food insufficiency risk among disadvantaged households differed by region. To be effective, governmental response to food insufficiency must address the varied local circumstances that contribute to these disparities.
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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 | 0.003 |
| 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 | 0.000 |
| 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".