Food insecurity amongst Canadian children with food allergy during the COVID-19 pandemic
Bibliographic record
Abstract
Food insecurity is a growing concern, that is currently estimated to affect 1 in 4 Canadian children. Due to the additional effort required for management and the disproportionate cost of allergy friendly foods, households with food allergy may be at increased risk of experiencing food insecurity. With this in mind, we aimed to describe and compare the prevalence of food insecurity amongst children in households managing pediatric food allergy between 2019, 2020 and 2022 using a repeated cross-sectional design. A total of 117 participants were recruited via social media between these three distinct timepoints, referred to as waves. All participants completed an anonymous online survey consisting of demographic questions and the Household Food Security Module from the Canadian Community Health Survey. Rates of child food insecurity were comparable between Waves 1 and 2 (34% and 35%, respectively; p=0.75), but, increased significantly between Waves 2 and 3 (35% and 56%, respectively; p=0.005). Amongst children identified as food insecure, the proportion who were marginally food insecure remained relatively stable, whereas, levels of moderate food insecurity appeared to increase, although not significantly. Conversely, the proportion classified as severely food insecure decreased across the waves, but again, this difference was not statistically significant. Our findings demonstrate an upward trend in child food insecurity levels, showcasing the need for a larger scale, longitudinal evaluation of the intersection between food allergy and food insecurity. We call on researchers and policy makers to attend to this important issue.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".