Associations between food allergy, country of residence, and healthcare access
Bibliographic record
Abstract
BACKGROUND: To date, little consideration has been given to access to allergy-related care, despite the fact that food allergy affects a considerable proportion of children. As such, the current study aimed to describe access to food allergy-related services in Canada and the United States (US). METHODS: Participants were recruited via social media from March-July 2021 and were asked to complete an online survey focused on food allergy-related medical care. Participants were Canadian and US residents who live with a child < 18 years old, with ≥ 1 food allergy. A series of logistic regressions were used to assess the associations between country of residence and type of allergy testing utilized during diagnosis. RESULTS: Fifty-nine participants were included in the analysis (Canadian: 32/59; 54.2%; US residents: 27/59; 45.8%). Relative to Canadian participants, US respondents were less likely to be diagnosed using an oral food challenge (OFC; OR 0.16; 95% CI 0.04; 0.75: p < 0.05). Compared to children diagnosed by age 2 years, those diagnosed at age 3 years and older were less likely to have been diagnosed using an OFC (OR 0.12; 95% CI 0.01; 1.01; p = 0.05). CONCLUSIONS: Access to food allergy-related services, varies between Canada and the US. We speculate that this variation may reflect differences in clinical practice and types of insurance coverage. Findings also underscore the need for more research centered on food allergy-related health care, specifically diagnostic testing, among larger and more diverse samples.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".