Ear Infection and Its Associated Risk Factors in First Nations and Rural School-Aged Canadian Children
Bibliographic record
Abstract
Background. Ear infections in children are a major health problem and may be associated with hearing impairment and delayed language development. Objective. To determine the prevalence and the associated risk factors of ear infections in children 6-17 years old residing on two reserves and rural areas in the province of Saskatchewan. Methodology. Data were provided from two rural cross-sectional children studies. Outcome variable of interest was presence/absence of an ear infection. Logistic regression analysis was conducted to examine the relationship between ear infection and the other covariates. Results. The prevalence of ear infection was 57.8% for rural Caucasian children and 43.6% for First Nations children living on-reserve. First Nations children had a lower risk of ear infection. Ear infection prevalence was positively associated with younger age; first born in the family; self-reported physician-diagnosed tonsillitis; self-reported physician-diagnosed asthma; and any respiratory related allergy. Protective effect of breastfeeding longer than three months was observed on the prevalence of ear infection. Conclusions. While ear infection is a prevalent condition of childhood, First Nations children were less likely to have a history of ear infections when compared to their rural Caucasian counterparts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".