More than Just Brushing: A Study of the Socioeconomic Impacts on Oral Health in Kindergarten Students
Bibliographic record
Abstract
Social determinants have been suggested as playing a role in the oral health status of kindergarten students. This research project examines the relationship between social factors (such as income, education, housing security, and family composition) and oral health indices (such as decayed, missing, extracted teeth (deft), debris, gingivitis, and decay type) in Brant County. The data collected by the Brant County Health Unit during 2011 and dissemination area data from the 2006 Canadian Census was used for this project. A semi-ecological analysis was performed using correlation, ANOVA, and Tukey post-hoc statistical tests. Overall, there was a significant correlation between high-risk demographic factors and high-risk oral health scores. In particular, housing related factors exhibited a significant increase between caries free and high caries groups, suggesting that housing related factors have an important impact on oral health. Furthermore, an increase in percentage of households receiving government transfers in higher decay groups suggests that access to dental insurance is not the only factor impacting of oral health, as almost all government transfer programs include a dental coverage component. These results suggest that dental programs should be targeted at areas of Brant County with high rates of families spending more than 30% of their income on housing, in addition to lower income areas. Furthermore, the findings suggest that the focus placed on the utilisation of care should be equal to that placed on access to care.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".