Racial/Ethnic Disparities in the Acceptance of Medicaid Patients in Dental Practices
Bibliographic record
Abstract
OBJECTIVES: Medicaid enrollees disproportionately experience dental disease and difficulties accessing needed dental care. However, little has been documented on the factors associated with the acceptance of new Medicaid patients by dentists, and particularly whether minority dentists are more likely to accept new Medicaid patients. We therefore examined the factors associated with the acceptance of new Medicaid patients by dentists. METHODS: We analyzed 2001 data from the Wisconsin Dentist Workforce Survey administered by the Wisconsin Division of Health Care Financing, Bureau of Health Information. We used descriptive statistics and logistic regression analysis to examine the factors associated with the outcome variable. RESULTS: Ninety-four percent of Wisconsin licensed dentists (n = 4,301) responded to the 2001 survey. A significantly higher likelihood of accepting new Medicaid patients was found for racial/ethnic minority dentists (35 versus 19 percent of White dentists) and dentists practicing in large practices (31 versus 16 percent for those in smaller practices). In the multivariable analysis, minority dentists [odds ratio (OR) = 2.06, 95 percent confidence interval (CI) = 1.30, 3.25] and dentists in practices with >3 dentists (OR= 2.25, 95 percent CI = 1.69, 3.00) had significantly greater odds of accepting new Medicaid patients. CONCLUSIONS: Racial/ethnic minority dentists are twice as likely as White dentists to accept new Medicaid patients. Dentists in larger practices also are significantly more likely than those in smaller practices to accept new Medicaid patients. These findings suggest that increasing dental workforce diversity to match the diversity of the general US population can potentially improve access to dental care for poor and minority Americans, and may serve as an important force in reducing disparities in dental care.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".