Child dental expenditures: 1996.
Bibliographic record
Abstract
PURPOSE: Because little has been reported about child dental expenditures, federal data were used to estimate dental care expenditures for U.S. children by age, sex, ethnic/ racial background, family income, parental education and parental employment. METHODS: Parentally reported data on dental expenditures and sources of expenditures were extracted from the most recent available federal healthcare expenditures studies, the 1996 federal Medical Expenditure Panel Survey (MEPS). Using the survey's large sample and complex design, these data represent the entire U.S. child population. RESULTS: Nearly 12 billion dollars were expended for children's dental care averaging $375 per child who obtained care. Overall sources of payment were 47% out of pocket, 45% insurance and 8% "other" including primarily Medicaid. Disproportionately litde spending was made on behalf of low-income and minority children despite their higher disease experience. The proportion of spending that was paid out of pocket was high for all groups of children including those eligible for Medicaid even though Medicaid prohibits cost sharing. CONCLUSIONS: Dental care for children accounts for approximately one-quarter of U.S. dental spending and is a major component of child health care costs. Income and racial disparities in expenditures favor higher income children despite Medicaid coverage for lower income children. High levels of reported out-of-pocket costs for Medicaid eligible children suggest that Medicaid fails to meet families' needs in obtaining care. Meeting the oral health needs of poor children will require considerably greater expenditures, particularly through improved Medicaid financing and administration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".