Parental perceptions of a novel subsidy program to address the financial burden of milk allergy: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Approximately 6-7% of Canadian children have food allergy. These families face substantial burdens due to the additional costs incurred purchasing allergy-friendly products necessary for management compared to families without food allergies. In the year prior to the COVID-19 pandemic, these costs were equivalent to an average of $200 monthly compared to families without food allergy. As food prices continue to rise, rates of food insecurity also increase, disproportionately affecting households with food allergy who have limited choices at food banks. METHODS: Families living or working in Winnipeg, Canada with an annual net income of about $70,000 or less the year prior to recruitment and a child under the age of 6 years old with a physician diagnosed milk allergy were recruited between January and February 2022. Participating families received bi-weekly home deliveries for six months, from March to August 2022, of subsidy kits containing ~$50 worth of milk allergy-friendly products. Semi-structured interviews, completed ± 2 weeks from the final delivery, were audio-recorded, transcribed verbatim, and analyzed thematically. RESULTS: Eight interviews, averaging 32 min (range 22-54 min), were completed with mothers from all different families. On average, mothers were 29.88 ± 4.39 years old and children were 2.06 ± 1.32 years old. All children reported allergies in addition to milk. Based on the data from these interviews, we identified 3 themes: food allergy causes substantial burden for families, "I have to get his allergy-friendly food first before getting to my basic needs", and perceived emotional and financial benefits of a milk allergy-friendly food subsidy program. CONCLUSIONS: This study, along with previous research, suggests that there is a need for assistance for families managing milk allergies. It also provides important information to inform development of programs which can address these financial challenges. Our in-kind food subsidy was perceived as having a positive impact on food costs and stress associated with food allergy management, however, parents identified a need for more variety in the food packages. Future programs should strive to incorporate a greater variety of products to address this limitation.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".