Dental Disparities among Low-Income American Adults: A Social Work Perspective
Bibliographic record
Abstract
The Centers for Disease Control and Prevention (CDC, 2012) defines health disparities as “preventable differences in the burden of disease, injury, violence, or opportunities to achieve optimal health that are experienced by socially disadvantaged populations” (para. 1). The lack of dental coverage available for low-income populations is a health disparity, and the affected populations deserve access to care. According to the Kaiser Family Foundation, “over a third (35 percent) of poor parents and 38 percent of poor adults without children were uninsured in 2013” (Majerol, Vann, & Rachel, 2014). Even as some gain coverage through state Medicaid expansions, it is estimated that only a quarter of states will offer comprehensive dental coverage (Nasseh, Vujicic, & O’Dell, 2013). Lack of access to dental care is not trivial. Mounting evidence suggests that poor oral health care leads to increased physical and mental health issues and greater cost to individuals and health care institutions. Ignoring dental health disparities in the United States has devastating social justice implications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".