Dental Treatment Needs in Vancouver Inner-City Elementary School-Aged Children
Bibliographic record
Abstract
Aims. To examine the dental treatment needs of inner-city Vancouver elementary school-aged children and relate them to sociodemographic characteristics. Methods. A census sampling comprising 562 children from six out of eight eligible schools was chosen (response rate was 65.4%). Dental treatment needs were assessed based on criteria from the World Health Organization. Results. Every third child examined needed at least one restorative treatment. A higher proportion of children born outside Canada were in need of more extensive dental treatments such as pulp care and extractions compared to the children born in Canada. There were no statistically significant differences in dental treatment needs between age, gender, or income groups or between children with or without dental insurance (Chi Squared P > 0.05). The best significant predictors (Linear Multiple Regression, P > 0.05) of higher dental treatment needs were being born outside Canada, gender, time of last dental visit, and family income. Having dental insurance did not associate with needing less treatment. Conclusion. A high level of unmet dental treatment needs (32%) was found in inner-city Vancouver elementary school-aged children. Children born outside Canada, particularly the ones who recently arrived to Canada, needed more extensive dental treatments than children born in Canada.
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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 | 0.002 | 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".