A scoping review on early childhood caries and inequalities using the Sustainable Development Goal 10 framework
Bibliographic record
Abstract
BACKGROUND: Social inequalities contribute to health disparities. This study aimed to map evidence on early childhood caries (ECC) related to the United Nations' Sustainable Development Goal 10 (SDG 10). METHODS: A scoping review was conducted in May 2024 following the PRISMA-ScR guidelines. A literature search was performed in PubMed, Web of Science, and CINAHL for studies published in English and addressing population level social inequalities. Studies measuring individual level of social inequalities were excluded as they were covered by other SDGs. However, studies incorporating individual measures as additional measures of population level social inequality were included. Retrieved papers were summarized, inductively analysed and a conceptual framework linking SDG 10 was developed. RESULTS: Of 452 studies retrieved, 42 met the inclusion criteria. Studies measured inequality among groups (deprivation, family income, indigenous communities, ethnicity, minority status) [14 studies], institutions (type of school, nursery or school facility, school poverty index, public primary health care units) [five studies], and inequality in communities (neighbourhood socio-economic status, Human Development Index, employment rate, income inequality, sanitary sewer and water supply, residents/household ratio, urban vs rural vs remote rural, accessibility index, location index, the slope index of inequality) [24 studies]. These levels of social inequalities were linked to higher prevalence of ECC; social and economic policies contributed to widening inequalities in ECC severity; and although effective interventions targeted at at-risk populations could reduce dental health disparities, study interventions differed by deprivation status. Six studies (14.3%) addressed SDG 10.1, 33 (78.6%) addressed SDG 10.2, 11 (26.2%) addressed SDG 10.3, and three (7.1%) addressed SDG 10.4. Fourteen studies (33.3%) addressed a combination of SDGs. The conceptual framework highlights the role of structural inequalities stemming from the cumulative impact of institutional decisions and systemic inequalities. CONCLUSION: This scoping review underscores the profound influence of social inequality on ECC through interactions between multi-level factors. Further research is needed to explore the links between ECC and other SDG 10 targets, especially in low- and lower-middle-income countries.
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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 | 0.047 | 0.124 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.055 | 0.046 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".