The impact of the COVID-19 pandemic on multicultural families with food allergy
Bibliographic record
Abstract
Background: Families with food allergy, in particular, have faced numerous challenges, often in the setting of financial and emotional stress during the coronavirus disease 2029 (COVID-19) pandemic. Objective: We examined the impact of the pandemic in a diverse population of families with food allergy. Methods: An online survey was administered between October 2020 and January 2021 through recruitment of adult caregivers of at least 1 child with food allergy. Survey responses were summarized by frequencies with proportions and medians with interquartile ranges or means plus or minus SDs. Results: = .032) was seen in significantly more caregivers with an income less than $200,000. Of the respondents, 76% experienced increased stress or discord within the home. Although becoming a member of a food allergy support group increased over time, significantly fewer African American respondents were members of a support group. The hospitalization rate for COVID-19 did not differ significantly between racial/ethnic groups. Conclusion: Our questionnaire has characterized the significant impact of economic as well as psychological stressors of the pandemic in a diverse population. Further studies on this topic are needed to help minimize the impact of future pandemics in a multicultural population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| 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".