A literature review of impact of social determinants of health on preventative oral health program design in remote communities: A focus on Spiti Valley, India
Bibliographic record
Abstract
Background and purpose: Dental caries is the most prevalent pediatric illness worldwide. It results from a complex interplay between biological and social determinants of health (SDH). The purpose of this review is 1) to understand the social determinants of health impacting dental caries burden in remote communities, using Spiti Valley, India as an example; 2) to understand the importance of using SDH to inform preventative oral health program (OHP) design, and lastly; 3) to provide best practice guidelines for implementing OHPs in remote communities worldwide. Methods: MEDLINE and PubMed databases were searched for English-language articles describing oral health programs implemented in remote communities around the world. Articles pertaining to OHPs that used preventative interventions to address pediatric dental caries in remote communities were included. Articles were excluded if the study sample included special needs children, and if the program lacked preventative interventions. Results: Remote communities around the world share many SDH factors, such as low income, limited education, limited availability and access to oral healthcare services, nutritious foods, clean water, and electricity. These factors are key to informing OHP design, and when addressed appropriately, can reduce a community’s dental caries burden. Conclusion: There is a continued need for preventative OHPs in remote pediatric caries burden remains high despite caries prevention strategies, that continue to contribute towards dental caries formation.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".