Ethnic disparities in children’s oral health: findings from a population-based survey of grade 1 and 2 schoolchildren in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Although oral health has improved remarkably in recent decades, not all populations have benefited equally. Ethnic identity, and in particular visible minority status, has been identified as an important risk factor for poor oral health. Canadian research on ethnic disparities in oral health is extremely limited. The aim of this study was to examine ethnic disparities in oral health outcomes and to assess the extent to which ethnic disparities could be accounted for by demographic, socioeconomic and caries-related behavioral factors, among a population-based sample of grade 1 and 2 schoolchildren (age range: 5-8 years) in Alberta, Canada. METHODS: A dental survey (administered during 2013-14) included a mouth examination and parent questionnaire. Oral health outcomes included: 1) percentage of children with dental caries; 2) number of decayed, extracted/missing (due to caries) and filled teeth; 3) percentage of children with two or more teeth with untreated caries; and 4) percentage of children with parental-ratings of fair or poor oral health. We used multivariable regression analysis to examine ethnic disparities in oral health, adjusting for demographic, socioeconomic and caries-related behavioral variables. RESULTS: We observed significant ethnic disparities in children's oral health. Most visible minority groups, particularly Filipino and Arab, as well as Indigenous children, were more likely to have worse oral health than White populations. In particular, Filipino children had an almost 5-fold higher odds of having severe untreated dental problems (2 or more teeth with untreated caries) than White children. Adjustment for demographic, socioeconomic, and caries-related behavior variables attenuated but did not eliminate ethnic disparities in oral health, with the exception of Latin American children whose outcomes did not differ significantly from White populations after adjustment. CONCLUSIONS: Significant ethnic disparities in oral health exist in Alberta, Canada, even when adjusting for demographic, socioeconomic and caries-related behavioral factors, with Filipino, Arab, and Indigenous children being the most affected.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".