Preventive Dental Care Utilization in Asian Americans in Austin, Texas: Does Neighborhood Matter?
Bibliographic record
Abstract
Although dental care is an essential component of comprehensive health care, a substantial proportion of the U.S. population lacks access to it. Disparities in dental care are most pronounced in racial/ethnic minority communities. Given the rapid population growth of Asian Americans, as well as the growing attention of neighborhood-level effects on health care use, the present study examines how individual-level variables (i.e., age, gender, marital status, ethnicity, education, place of birth, length of stay in the U.S., dental insurance, and self-rated oral health) and neighborhood-level variables (i.e., poverty level, density of Asian population, dentist availability, and Asian-related resources and services) contribute to predicting the use of preventive dental care in a sample of Asian Americans in Austin, TX. This study adds to the growing literature on the effect of neighborhood-level factors on health care as sources of disparities. Those living in the Census area with higher level of available dentists were more likely to use preventive dental care services. Findings suggest the importance of the location (proximity or accessibility) to dental clinics. In a planning perspective for health care policy, identifying the neighborhood with limited healthcare services could be a priority to diminish the disparity of the access.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".