Medical-Dental Integration for Vulnerable Populations: Addressing Social Determinants and Complex Care Needs – A Narrative Review
Bibliographic record
Abstract
Background: Vulnerable populations, including those experiencing homelessness, refugees, and the elderly in long-term care (LTC), suffer disproportionately from poor oral health and its systemic sequelae, exacerbated by fractured care systems and profound social determinants of health (SDOH). The historical schism between medical and dental care creates insurmountable barriers for these groups, leading to preventable suffering, dignity loss, and costly emergency department (ED) utilization for untreated dental pain and infection. Aim: This narrative review synthesizes evidence from 2010-2024 on integrated medical-dental care models for vulnerable populations, analyzing the roles of sociology, nursing, dental laboratories, health assistants, pharmacy, and health security in delivering equitable, person-centered care. Methods: A comprehensive search of PubMed, Scopus, CINAHL, and sociology databases was conducted. Thematic analysis integrated literature from public health, nursing, dental science, social sciences, and health services research. Results: Effective models—such as co-located clinics, embedded dental services in shelters/LTC, and mobile units—demonstrate improved access, better management of chronic diseases (e.g., diabetes), and reduced acute care visits. Success is contingent on addressing sociological barriers (stigma, trust), utilizing health assistants as navigators, incorporating oral health into nursing assessments, providing affordable prosthetics, and ensuring pharmaceutical coordination. Conclusion: Medical-dental integration is not merely a clinical convenience but an ethical imperative for health equity. Its realization requires a deliberate, team-based approach that dismantles professional siloes, actively counters structural stigma, and embeds oral health within a holistic framework of social care and crisis preparedness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".