Using GIS to Analyze Inequality in Access to Dental Care in the District of Columbia
Bibliographic record
Abstract
BACKGROUND: Access to dental care in mixed-race and predominantly African American wards in the District of Columbia (DC) was investigated in relation to community development. METHODS: This study used high-resolution geographic information system (GIS) tools to map all general dentistry and periodontal practice locations in DC wards. The spatial analysis contextualized each ward's land use and demographic data obtained from DC government reports. FINDINGS: The analysis revealed inter-ward inequity in dental care access, which was measured by proximity to and number of dental clinics in each DC ward. Residents in affluent wards had access to many dental practices and superior amenities. Residents in wards poorly served by public transportation and with few resources had few, if any, dental clinics. CONCLUSIONS: Dental practices are inequitably distributed across DC wards. DC policy should prioritize community development-specifically, resource allocation and community outreach-to promote health equity and improve access to and quality of dental care among residents of color.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".