Diagnosis and treatment of food allergies in off‐reserve Aboriginal children in Canada
Bibliographic record
Abstract
Food allergies have emerged as an important public health risk in many countries, Canada included, affecting children at a disproportionate rate. However, we know very little about food allergies among Aboriginal children in Canada, who are often characterized as representing a particularly vulnerable population with respect to health and life conditions more generally. Our objective was to address this gap by exploring the prevalence and management of food allergies in this population. Data from the 2006 Aboriginal Children's Survey were used for this analysis. Descriptive analyses were undertaken to determine the prevalence of diagnosed food allergies among off‐reserve Aboriginal children, and logistic regression was used to explore factors associated with diagnosis, and the determinants of receiving treatment. Estimates of prevalence (2.9%) appear lower than the general population in Canada. Controlling for demographic and socioeconomic factors, co‐morbidity of asthma and access to family physicians and specialists (e.g., allergists) most strongly predicted both prevalence and treatment. Lower prevalence rates suggest either truly lower rates or lower rates of detection in this population. Access to treatment appears most significant for diagnosis and treatment for this population, raising important directions for future research addressing disparities in the management of food allergies among Aboriginal children.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".