Experiences of Northern Ontario Nurse Educators in Adapting Teaching Methods
Bibliographic record
Abstract
Oral health disparities continue to present a significant public health challenge for older adults in the United States, particularly among racial minority and low-income populations. Despite national discussions around healthcare equity, there remains limited empirical insight into how race and income influence dental service utilization among Medicare beneficiaries. In this quantitative, cross-sectional study, the extent to which these demographic factors predicted oral healthcare utilization, using nationally representative data from the 2021 Medicare Current Beneficiary Survey, was assessed. Guided by the social determinants of health framework, chi-square tests, binary logistic regression, and negative binomial regression were performed to analyze the data. Findings revealed that both race and income were significant predictors of whether older adults used dental care. Non-Hispanic Black beneficiaries had 49% lower odds of utilization compared to non-Hispanic White counterparts (OR = 0.51, p < .001), and those with household incomes below $25,000 had 70% lower odds of utilization compared to higher income adults (OR = 0.30, p < .001). Among utilizers, income remained significant, with higher income adults reporting more visits (Incidence Rate Ratio (IRR) = 1.37, p=.015), while race was not significant once access was established. These results highlight systemic inequities in access and underscore the need for policy reforms, such as expanding Medicare dental benefits and implementing culturally responsive outreach strategies, contributing to the evidence base needed to inform equitable health interventions for aging populations and promote positive social change.
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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".