Changes in Dental Care Utilization and Barriers among Mexican Americans: Evidence from NHANES III and 2011-2020
Bibliographic record
Abstract
OBJECTIVE: This study examines trends and factors influencing dental care utilization among Mexican Americans from 1988 to 2020. METHODS: Data from the National Health and Nutrition Examination Survey (NHANES) III and 2011-2020 were analyzed using logistic regression to assess the impact of immigrant status and socioeconomic status on dental visits among Mexican Americans. Slope tests were applied to examine the different patterns across cohorts. RESULTS: Compared to NHANES III, the 2011-2020 waves showed a decrease in the proportion of irregular dentist visits (from 0.67 to 0.54) and an increase in foreign-born status (0.46 to 0.57) and college education (0.19 to 0.33). The 2011-2020 sample was also older than that of NHANES III. Regression analyses revealed that speaking Spanish was associated with higher odds of irregular dentist visits, while higher education, family income, and health insurance were associated with lower odds of irregular dentist visits. The protective effect of foreign-born status on dental care utilization has increased over time, while the protective effects of college education and higher family income have diminished. POLICY IMPLICATIONS: Expanding language services and increasing health insurance coverage are critical to addressing barriers to dental care for Mexican Americans.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".