An investigation of a novel milk allergy-friendly food supplement program
Bibliographic record
Abstract
Introduction: Compared to households not managing food allergy, households managing food allergy are faced with greater direct and indirect costs. To address these cost burdens, we developed and piloted a milk allergy-friendly food supplement program for lower- and middle-income households managing a dairy allergy in a child age <6 years. Herein, we aimed to evaluate to the impact of this program on food costs, food security, and caregiver mental health using a longitudinal design. Methods: Participants living in or near the city of Winnipeg, in Manitoba, Canada were recruited from January to February 2022 via social media, word-of-mouth, and a database maintained by the principal investigator. Consenting participants took part in a 6-month allergen-friendly food supplement program that provided them with biweekly deliveries of allergen-friendly foods free of charge. To evaluate the impact of the program on food costs, food security, and well-being, participants completed a series of questionnaires at baseline, mid-point, and at the end of the program. Changes in these variables were assessed via a series of Friedman tests. Results: The final sample was comprised of 8 households. Relative to baseline, participants reported higher total direct food costs at midpoint (+5.6%) and endpoint (+13.5%), but these changes did not reach statistical significance. In contrast, total indirect food costs decreased over the course of the study relative to baseline (midpoint = -28.2%; endpoint = -18.5%), but the changes were not found to be statistically significant. Participants did, however, report a statistically significant decrease in costs related to lost time from work or school as a result of their child's food allergy at endpoint relative to baseline (-100%). Few changes in food security, caregiver well-being, or child food allergy quality of life were noted. Discussion: The provision of allergen-friendly foods helped keep grocery costs below the pace of inflation. Participants also reported reduced costs associated with missed time from work or school as a result of their child's food allergy. Despite these encouraging findings, a relatively high proportion of the current sample reported experiencing food insecurity throughout the study period, suggesting that additional financial support for families is needed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".