Oral Health Disparities in United States and Canadian Immigrant Population and Low-income Familie
Bibliographic record
Abstract
Even though our country's oral health has improved since 1960s; however, the access to these improvements is not equally distributed among all Americans.According to the Center for Disease Control and Prevention, more people are unable to afford dental care than other types of health care.Specifically, in 2015, 29% of the people in the United States have no health insurance, of out which 62% are older adults.Many low-income adults do not have public dental insurance, which includes immigrant population.Specifically, Medicaid programs are not required to provide dental benefits to retiring adults.As a result, currently, there are fifteen states that provide no dental coverage or only emergency coverage for adults who are on Medicaid.Moreover, 40% of low-income and non-Hispanic African American adults have untreated tooth decay.Among children between 2 and 5 years, about 33% of Mexican American and 28% of non-Hispanic African American have had cavities in their primary teeth, compared to 18% of non-Hispanic White children.For children between 12 and 19 years old, about 70% of Mexican American children have had cavities in their primary teeth, compared with 54% of non-Hispanic White children.This paper will discuss the need to respond to these statistics.These oral health disparities do not only have health consequences, but also have social and economic impacts that cannot be overlooked (CDC.gov,2021).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".