Food-Seeking Behaviors and Food Insecurity Risk During the Coronavirus Disease 2019 Pandemic
Bibliographic record
Abstract
OBJECTIVE: Food insecurity risk increases among disaster-struck individuals. The authors employed the social determinants of health framework to (1) describe the characteristics and food-seeking behaviors of individuals coping with the coronavirus disease 2019 pandemic and (2) evaluate the relationship between these factors and food insecurity risk. DESIGN: A cross-sectional Qualtrics survey was administered May 14-June 8, 2020. PARTICIPANTS: Adults living in New York were recruited online (n = 410). MAIN OUTCOME MEASURE: Food insecurity risk. ANALYSIS: Logistic regression analyses were conducted using a model-building approach. RESULTS: A total of 38.5% of the sample was considered food insecure after the coronavirus disease 2019 outbreak. The final model revealed that not knowing where to find help to acquire food, reporting that more food assistance program benefits would be helpful, being an essential worker, having general anxiety, and being a college student were risk factors for food insecurity regardless of demographic characteristics. CONCLUSIONS AND IMPLICATIONS: With more individuals experiencing food insecurity for the first time, there is a need for enhanced outreach and support. The findings complement emerging research on food insecurity risk during and after the pandemic and can help to inform food assistance programs and policies.
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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 | 0.001 |
| 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 | 0.001 | 0.002 |
| 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".