A Systematic Review and Meta-Analysis on Oral Health Disparities Among the Indigenous Paediatric Population
Bibliographic record
Abstract
There is a knowledge gap in the literature regarding oral health disparities (OHD) in minority and indigenous (IG) paediatric cohorts that needs to be addressed. Disparities in oral health among children are a pressing concern, highlighting inequities in access to dental care and meeting needs. The current systematic review aims to provide a comprehensive synthesis of the prevailing understanding of OHD in the minority and IG strata. A meticulous search strategy was formulated by a team of reviewers to identify pertinent studies from databases of PubMed, MEDLINE, Scopus, Google Scholar and EMBASE. Data extraction and article selection strictly adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Newcastle-Ottawa Scale (NOS) was employed to evaluate the methodological quality of the studies included. Review Manager version 5.4 was used to synthesise quantitative data. A total of five cross-sectional studies were included in the final analysis. The findings consistently demonstrated the existence of racial and socioeconomic disparities in oral health across varying age groups and geographical locations in the defined population. Significant disparities in oral health outcomes were observed between IG and non-IG populations, with IG and minority groups exhibiting a heightened vulnerability to oral health challenges. Through a meta-analysis of the compiled data, a statistically significant association was established between children (being a member of a minority group) and unmet oral health needs. Socioeconomic status (SES) and maternal education were factors that showed a significant impact on oral health disparity. All studies were graded to be of the low-risk category based on the NOS risk of bias tool. This review successfully identified several influential factors contributing to oral health disparities, such as cultural practices, dietary patterns and access to oral healthcare services. Additionally, discernible differences in oral health status were evident between IG and non-IG children, with IG children enduring a greater burden of oral health difficulties. These findings underscore the imperative for targeted interventions and policy measures aimed at addressing the specific oral health needs of minority and IG paediatric populations, with the overarching goal of mitigating the existing disparities.
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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 | 0.026 | 0.078 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".