A Survey of Canadian Dietitians on Identification of Infants at High Risk of Food Allergy and Frequency of Allergenic Food Consumption
Bibliographic record
Abstract
Purpose: To assess knowledge of Canadian dietitians on the topics of food allergy and food allergy prevention guidelines, including introduction of allergenic solids to infants at risk of food allergy. Methods: An online survey was distributed via email listservs targeting Canadian dietitians. Results: In total, 144 of 261 dietitians completed the survey (60.5%). Respondents recommend introduction of peanut (89.5%) and allergenic solids (91.2%) within the recommended age of 4–6 months for infants at high risk of food allergy, but only 26.2% recommend offering peanut three times per week once it has been introduced. In identifying what constitutes an infant at high risk of developing peanut allergy, dietitians expressed lower comfort levels and lower number of correct responses. Conclusions: Dietitians demonstrated they are up to date regarding the timing of introduction of allergenic solids, but not the frequency of consumption once introduced, for infants at high risk of food allergy. They also expressed low comfort level identifying risk factors for peanut allergy. There are opportunities for further education of dietitians, as well as potential to further utilize dietitian services for the benefit of patients with food allergy or who are at risk for food allergy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".