The Relations between Systems of Oppression and Oral Care Access in the United States
Bibliographic record
Abstract
We applied a structural intersectionality approach to cross-sectionally examine the relationships between macro-level systems of oppression, their intersections, and access to oral care in the United States. Whether and the extent to which the provision of government-funded dental services attenuates the emerging patterns of associations was also assessed in the study. To accomplish these objectives, individual-level information from over 300,000 respondents of the 2010 US Behavioral Risk Factor Surveillance System was linked with state-level data for 2000 and 2010 on structural racism, structural sexism, and income inequality, as provided by Homan et al. Using multilevel models, we investigated the relationships between systems of oppression and restricted access to oral health services among respondents at the intersections of race, gender, and poverty. The degree to which extended provision of government-funded dental services weakens the observed associations was determined in models stratified by state-level coverage of oral care. Our analyses bring to the fore intersectional groups (e.g., non-Hispanic Black women and men below the poverty line) with the highest odds of not seeing a dentist in the previous year. We also show that residing in states where high levels of structural sexism and income inequality intersect was associated with 1.3 greater odds (95% confidence interval, 1.1-1.5) of not accessing dental services in the 12 mo preceding the survey. Stratified analyses demonstrated that a more extensive provision of government-funded dental services attenuates associations between structural oppressions and restricted access to oral health care. On the basis of these and other findings, we urge researchers and health care planners to increase access to dental services in more effective and inclusive ways. Most important, we show that counteracting structural drivers of inequities in dental services access entails providing dental care for all.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".