Parental Motivation for Introducing Babies’ First Foods and Common Food Allergens
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Findings from the Learning Early About Peanut trial prompted a shift in clinical practice guidelines to support the early and continuous introduction of allergenic foods to reduce the risk of food allergy. Our study aimed to describe the reasoning behind parents' decisions on the introduction of first foods to their infants and the age at which parents first introduced common allergens. METHODS: Parents of a child aged <18 years old with ≥1 food allergy, who lived in either Canada or the United States, were recruited via social media between March 2021 and February 2022 to participate in an online, anonymous survey. Data were analyzed descriptively and using binary logistic regression. RESULTS: A total of 42 parents completed the survey, the majority being mothers (40/42; 95.2%). Children were, on average, 6.9 ± 0.7 years old. In total, 47.6% of parents introduced first foods between ages 4-5 months, whereas 52.4% introduced first foods at 6 months or older. Cereals were the most frequently introduced first food (54.8%; 23/42). Most parents (71.9%) selected first foods to introduce based on guidance from healthcare providers. CONCLUSIONS: For many parents, guidance from healthcare providers is the most influential factor in determining when and what to introduce as first foods to their infant. Although the paradigm shift in infant feeding practices has been well accepted by healthcare providers, this information has not been adequately translated to the general population. This emphasizes the need for healthcare providers to educate and reinforce the importance of early introduction to reduce the risk of food allergy.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".