Bibliographic record
Abstract
Inadequate access to dental care is a major problem for Georgia's low-income children.Dental caries (tooth decay) is the single most common chronic childhood disease -5 times more common than asthma.Pain and suffering due to untreated diseases lead to problems in eating, speaking, and paying attention in school.According to Georgia's teachers, dental problems are one of the two most frequently cited reasons that children miss school (along with vision problems.)Low-income families seeking dental care in Georgia are very frustrated.Insurance, either Medicaid or PeachCare for Kids, is not a guarantee of access to care.Only about one quarter of Medicaid and PeachCare children (200,000 out of 800,000 children) were able to see a dentist in FY2000, though more than twice that number sought care.Low-income families without insurance are even less likely to get dental care, according to national studies.In Georgia, the uninsured (about 300,000 children) only have access through the Oral Health Program of the Department of Public Health.In FY2000, it provided screening and referral services to 81,000 children, but it can not treat many of the children who have dental diseases.Georgia has begun addressing the dental crisis by raising dentists' Medicaid and PeachCare rates.Rate hikes in July 1998 and July 2000 have made public payments competitive with private payments, addressing the number one reason dentists give for not seeing more public patients.In this paper, we examine the likely impact of recent changes and propose actions that are needed to gain access for every child.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.458 | 0.178 |
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".