Temporal examination of adult food insecurity amongst Canadian families managing food allergy
Bibliographic record
Abstract
BACKGROUND: Pediatric food allergy is associated with excess familial food costs compared to families without allergy. Since the start of the COVID-19 pandemic, food prices have increased substantially. OBJECTIVE: To understand the temporal pattern of food insecurity amongst Canadian families with food allergy from the year prior to the pandemic, through May 2022. METHODS: Using data collected electronically from families reporting food allergy using a validated food security questionnaire, we estimated food insecurity, including categories of food insecurity (marginal, moderate, secure) in the year prior to the pandemic (2019; Wave 1), and the first (2020; Wave 2) and second years of the pandemic (2022; Wave 3). RESULTS: Participants in all waves were commonly in 2 + adult, 2 child households. Less than half of participants (Waves 1-3: 45.7%, 31.0%, and 22.9%, respectively) reported household incomes below the median Canadian. Common allergies were milk, eggs, peanuts and tree nuts. In Wave 1, 22.9% of families reported food insecurity; corresponding numbers at Waves 2 and 3 were 30.6% and 74.4%, respectively, representing an overall increase of 225.6%, including notable increases in severe food insecurity. CONCLUSION: Canadian families with pediatric food allergy report higher rates of food insecurity compared to the general Canadian population, especially during the pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".