Race disparities in dental care use from adolescence to middle adulthood in the USA
Bibliographic record
Abstract
BACKGROUND: This study examines the longitudinal patterns of dental care use from adolescence to middle adulthood (ages 11-43) and investigates racial and ethnic disparities in these patterns. METHODS: Data from Waves I through V of the National Longitudinal Study of Adolescent to Adult Health (1993-2018; ages 11-43). Semiparametric group-based trajectory model identified distinct dental care use trajectories. Multinomial logistic regression was used to estimate membership in these trajectory groups by race/ethnicity while accounting for covariates, including socioeconomic status, biological sex, nativity and unmet healthcare needs. RESULTS: The analysis identified four distinct dental care use trajectories (1): Intermittent decreasing dental care use (37.9%), (2) intermittent increasing dental care use (22.5%), (3) high dental care use (22.5%) and (4) low dental care use (17.0%). Non-Hispanic black and Hispanic respondents were more likely than non-Hispanic white respondents to belong to low dental care use and intermittent decreasing dental care use groups relative to high dental care use. Additionally, non-Hispanic black respondents were more likely than non-Hispanic white respondents to belong to the Intermittent Increasing Dental Use group. Higher socioeconomic status was inversely associated with low and intermittent use group membership. Males and those with unmet healthcare needs at Wave I were also more likely to belong to trajectories with low and intermittent dental care use. CONCLUSIONS: Findings reveal persistent racial disparities in dental care use from adolescence into adulthood. Further research is needed to understand the individual and structural factors perpetuating racial disparities in dental care use over the life course.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".