Trick or treat? – when children with childhood food allergies lead parents into unhealthy food choices
Bibliographic record
Abstract
BACKGROUND: This study examines the relationships between childhood food allergy and parental unhealthy food choices for their children across attitudes towards childhood obesity as mediators and parental gender, income and education as potential moderators. METHODS: We surveyed parents with at least one child between the ages of 6 and 12 living in Canada and the United States. We received 483 valid responses that were analysed using structural equation modelling approach with bootstrapping to test the hypothetical path model and its invariance across the moderators. RESULTS: The analysis revealed that pressure to eat fully mediated the effects of childhood food allergy and restriction on parental unhealthy food choices for their children. Finally, we found that parental gender moderated the relationship between childhood food allergy and the pressure to eat. CONCLUSIONS: The paper contributes to the literature on food allergies among children and the marginalisation of families with allergies. Our explorative model is a first of its kind and offers a fresh perspective on complex relationships between variables under consideration. Although our data collection took place prior to Covid-19 outbreak, this paper bears yet particular significance as it casts light on how families with allergies should be part of the priority groups to have access to food supply during crisis periods.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".