NOURISH-US: a mixed-methods, randomized crossover study of a program designed to reduce the financial burden of food allergy
Bibliographic record
Abstract
BACKGROUND: Food allergy imposes considerable financial costs on families, but few programs are available in Canada to offset these costs. To fill this gap, we developed, piloted, and evaluated a program designed to address the financial burden of food allergy. METHODS: The current study employed the use of an unblinded, crossover design. Participating families who began the study in the case condition received biweekly deliveries of food packages for 2 months, while those in the control condition received recipes and educational materials. Following the initial study period, the groups entered a one-month washout period and the conditions were reversed. During both conditions, an adult member of each participating family ("caregivers") responded to a quantitative cost measure and completed a qualitative interview. Quantitative data were analysed using a series of linear mixed models. Qualitative data were analysed using thematic analysis. RESULTS: A total of 14 participants were randomized to a sequence using Stata. However, 5 participants were dropped from the final quantitative sample due to a failure to complete one or more set of quantitative measures. Caregivers included in the final quantitative sample were 32.1 years old, on average, overwhelmingly female (89%), and had annual, after-tax, household income of $52,660.00 (SD=$23,188.92; CAD). Target children were largely under six years old (89%) and were evenly split between boys (44%) and girls (44%). Milk (67%), peanut (67%), and egg (67%) allergies were most common. Quantitative results revealed participants had non-significantly lower indirect costs in the food delivery condition ($724.56 vs. $797.83), largely because of lower food preparation costs ($561.41 vs. $656.15). In contrast, participants reported non-significantly higher direct costs when they were receiving the food packages ($678.47 vs. $655.56). Findings from the qualitative interviews suggest that this increase may reflect the fact that participants purchased more expensive grocery items in response to the cost savings afforded by the program. CONCLUSIONS: Participants derived several benefits from the program, but more research is needed to better understand how to maximize the impact of programs like NOURISH-US and to identify families most in need of financial support.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".