Predictors of Food and Water Stockpiling During the COVID-19 Pandemic Among Latinos and Non-Latino Black People
Bibliographic record
Abstract
OBJECTIVE: The study examined factors associated with food and water stockpiling (FWS) during the COVID-19 pandemic. METHODS: A secondary analysis of online survey data collected in two waves: April 2020 (wave 1) and June/July 2020 (wave 2), was conducted through REDCap web application. A total of 2,271 Non-Latino Black and Latino adults (mean age: 36.8 years (SD = 16.0); 64.3% female) living in Illinois were recruited. Participants self-reported if they stockpiled food and/or water (FWS) seven days prior to survey completion because of the pandemic. Logistic regression was used to determine if each variable was associated with the odds of reporting FWS. RESULTS: Nearly a quarter (23.3%) of participants reported FWS. The adjusted model revealed that odds of FWS increase as the number of household members increased (OR: 1.21; 95% CI: 1.05-1.41). Odds of FWS were lower among participants who were not self-quarantining compared to those self-quarantining all the time (OR: 0.32; 95% CI: 0.17 - 0.62). Furthermore, individuals with lower levels of concern about COVID-19 had lower odds of FWS than those extremely concerned. CONCLUSIONS: Household size, self-quarantine status, and concern about COVID-19 were significantly associated with FWS. These findings highlight the need to address the concerns of marginalized individuals to promote healthy behaviors.
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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 | 0.000 | 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) | 0.003 | 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".