Changes in food-related costs during the COVID-19 pandemic among families managing food allergy
Bibliographic record
Abstract
Background The COVID-19 pandemic has affected the supply, cost, and demand for certain foods, but it is not clear how these changes have affected food-allergic households. Objective To describe the changes in food-related costs that have followed COVID-19, as reported by higher- and lower-income households with a food-allergic member. Methods Between May 1-June 30, 2020, Canadian households, with at least one food-allergic member, completed an online survey on food shopping and preparation habits before and during the COVID-19 pandemic. The sample was divided into binary groups, either higher or lower than the sample median income. Data were analyzed using descriptive statistics and multiple regression. Results The sample was comprised of 102 participants (i.e., 51/ income group). The three most common food allergies amongst both groups were peanuts, tree nuts and milk. Since the start of the pandemic, both groups reported greater monthly direct grocery costs, although costs amongst the higher-income group were twice as high as the lower-income group ($212.86 vs. $98.89, respectively). Indirect food preparation costs were similarly elevated. Higher-income households with food procurement difficulties reported increased indirect shopping costs following the outbreak of COVID-19, whereas those without such difficulties reported decreased costs. Lower-income households with allergies to milk, wheat, or eggs (i.e., staple allergy) experienced a larger change in indirect food preparation costs following the outbreak of COVID-19 relative to those with other food allergies ($244.58 vs. –$20.28, respectively; p = 0.03). Conclusion Both higher and lower income households with food allergy reported greater direct food costs and indirect food preparation costs following the COVID-19. Households with staple allergy and those with difficulties finding their typical food items were particularly affected.
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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 | 0.006 |
| 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 | 0.001 |
| 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".