Barriers to Medicaid Participation among Florida Dentists
Bibliographic record
Abstract
BACKGROUND: Finding dentists who treat Medicaid-enrolled children is a struggle for many parents. The purpose of this study was to identify non-reimbursement factors that influence the decision by dentists about whether or not to participate in the Medicaid program in Florida. METHODS: Data from a mailed survey was analyzed using a logistic regression model to test the association of Medicaid participation with the Perceived Barriers and Social Responsibility variables. RESULTS: General and pediatric dentists (n=882) who identified themselves as either Medicaid (14%) or Non-Medicaid (86%) participants responded. Five items emerged as significant predictors of Medicaid participation, with a final concordance index of 0.905. Two previously unreported barriers to participation in Medicaid emerged: 1) dentists' perception of social stigma from other dentists for participating in Medicaid, and 2) the lack of specialists to whom Medicaid patients can be referred. CONCLUSIONS: This study provides new information about non-reimbursement barriers to Medicaid participation.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".