Teledentistry models led by dental hygienists in underserved communities: a literature review.
Bibliographic record
Abstract
Objective: This thematic literature review explores current teledentistry models led by dental hygienists in rural, underserved areas. Its primary aim is to critically evaluate the impact of these models on access to care, clinical and economic outcomes, and workforce development. This review provides valuable insights for health care professionals, policymakers, and researchers interested in oral health care and telehealth. Methods: A structured literature search was conducted in the following databases: Ovid/MEDLINE, CINAHL, and Dentistry & Oral Sciences (EBSCOhost). The search terms were organized into 3 conceptual categories: population, intervention, and provider. Only peer-reviewed articles on teledentistry-based care delivered by dental hygienists or comparable providers, such as dental therapists, in underserved or rural settings were included. Results: Ten studies met the inclusion criteria for review: all explored the implementation of teledentistry models in underserved communities. These studies used a variety of qualitative, descriptive, and mixed-method designs. The literature highlighted effective service delivery models, economic advantages, positive patient and provider outcomes, and policy limitations. Discussion: Five key themes emerged: service delivery models and access to care, economic and clinical efficiency, regulatory and policy environments, workforce development and training, and systemic barriers to program sustainability. Conclusion: Teledentistry initiatives led by trained dental hygienists are a cost-effective and scalable means of improving access to care for underserved rural or Indigenous populations. Expanding these models across rural and urban regions and integrating this implementation into policy reform, workforce investment, and mainstream dental and dental hygiene education should be considered an urgent priority.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".