Oral Health Status and Oral Health–Related Quality of Life of First Nations and Metis Children
Bibliographic record
Abstract
OBJECTIVES: To assess the oral health status and oral health-related quality of life (OHRQoL) of young First Nations and Metis children. METHODS: This cross-sectional study assessed the oral health status of Indigenous children <72 mo of age while their parents/caregivers completed a questionnaire, including the Early Childhood Oral Health Impact Scale (ECOHIS), to assess OHRQoL. Analysis included descriptive statistics, bivariate analyses, and multiple regression. A P value ≤0.05 was considered significant. RESULTS: Overall, 146 children were recruited with a mean age of 40.1 ± 21.2 (SD) months, and 49% were male. Among First Nations children, 65.4% had early childhood caries (ECC) as compared with 45.2% among Metis children (P = 0.025). However, there was no statistically significant difference in the prevalence of severe ECC (S-ECC) between First Nations and Metis children (60.6% v. 42.9%, P = 0.051). The mean decayed, missing, and filled primary teeth (dmft) score was 4.9 ± 5.3 (range 0-20), and the mean decayed, missing, and filled surfaces (dmfs) score was 14.5 ± 20.4 (range 0-80). The total mean ECOHIS score was 4.4 ± 5.9 (range 0-25), while the mean Child Impact Section and Family Impact Section scores were 2.6 ± 4.0 (range 0-10) and 1.8 ± 2.8 (range 0-8), respectively. Multiple linear regression showed S-ECC was associated with total mean ECOHIS scores (P = 0.02). Higher total mean ECOHIS scores (which indicates poorer OHRQoL) were observed in children with ECC compared with caries-free children (5.8 v. 2.4, P = 0.0001). CONCLUSION: Oral health disparities such as ECC and reduced OHRQoL exist among many First Nations and Metis children in Manitoba. This is the first Canadian study exploring the OHRQoL of Indigenous children in addition to their oral health status. KNOWLEDGE TRANSFER STATEMENT: This study is the first to report on the oral health-related quality of life and its relationship to early childhood caries (ECC) among young Canadian First Nations and Metis children. Metis children are just as likely to suffer from severe ECC than First Nations children. The findings of this study have informed community-based and community-developed oral health promotion and ECC prevention activities.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".