Evaluating the Impact of Dentists on Preventive Oral Healthcare and Community Well-Being: A Systematic Review
Bibliographic record
Abstract
Preventive oral healthcare is a critical component of public health, and dentists play a central role in reducing the global burden of dental diseases and enhancing community well-being. This systematic review examines evidence from 2016 to 2025 to evaluate the impact of dentist-led preventive interventions across clinical, educational, community, and system-level domains. Findings demonstrate that preventive dental services—including fluoride varnish, sealants, and early diagnostic screenings—significantly decrease caries incidence, periodontal disease progression, and tooth loss. Dentist-delivered education and behavioral counseling were found to improve oral hygiene practices, dietary choices, and oral health literacy, contributing to sustainable long-term health behaviors. Community-based programs and outreach initiatives led by dentists increased access to preventive care, particularly among underserved populations, while interdisciplinary collaborations and innovations such as teledentistry enhanced early detection of oral and systemic conditions. Collectively, the evidence shows that dentists contribute not only to improved oral health outcomes but also to broader community well-being through enhanced quality of life, reduced healthcare costs, and strengthened public health systems. Despite progress, disparities in access and long-term outcome data remain areas requiring further attention. Strengthening preventive dentistry is essential for promoting equitable and sustainable community health
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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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".