Sociodemographic Changes and Oral Health Inequities: Dental Workforce Considerations
Bibliographic record
Abstract
BACKGROUND: By midcentury, the US population will be remarkably more racially and ethnically diverse, with a dramatic increase in the proportion of older adults. This report addresses ongoing oral health disparities and inequitable access to care related to these changes, with emphasis on implications for the workforce, taking note of effects of the COVID-19 pandemic. RELEVANT CONSIDERATIONS: Considering that social determinants shape health behaviors, reflection on the most effective type of dental workforce should take into account population characteristics and the relationship of oral health with overall health and general well-being. The dental workforce composition will need to mirror changing demographics, and effective dental health teams will be characterized by cultural competence, humility, readiness, and capacity to adapt to changes. In addition, the influence of social histories and the pandemic on health and dental care utilization is important. Equally important are the inclusion of oral health literacy in treatment planning and disease prevention, as well as oral health-related quality of life in considering outcomes of care. Providing patient-centered care for a diverse population requires tailored treatment modalities, as well as intra- and interprofessional approaches. In this way, the whole person can be cared for, including those with special health care needs, whether related to chronic disease, mental health conditions, or behavioral, physical, and social differences. CONCLUSIONS: Changing demographics will affect the delivery of oral health care, including who can best provide care and how, what the needs are, and in what ways prevention and treatment can most effectively be accomplished. The education of dentists must address unmet population needs, including for those with special health care concerns and older adults. These population groups are influenced by a variety of social determinants, and provision of services may need to occur in alternative care delivery settings. Identifying and addressing the needs of every patient within this broad array of new requirements will challenge dental professionals to redefine what it means to be a health care practitioner. KNOWLEDGE TRANSFER STATEMENT: This article describes how sociodemographic changes in the United States will challenge the dental workforce in new ways and points to research and practice needs to address these challenges. Oral health disparities and the changing oral health care needs of patients from diverse and underserved groups are discussed, with a focus on the implications for delivery of care and policies that are needed to improve oral health outcomes for all.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".