Orthodontists’ and Orthodontic Residents’ Education in Treating Underserved Patients: Effects on Professional Attitudes and Behavior
Bibliographic record
Abstract
The U.S. surgeon general's report on oral health in 2000 stressed the importance of providing dental care for underserved patient groups. Given that orthodontic treatment is less likely to be covered by dental plans than other procedures and is often considered an elective treatment, it is not surprising that access to orthodontic care is an especially severe problem for underserved patient groups. The purpose of this study was to explore the degree to which orthodontic residents and orthodontists perceived that their graduate orthodontic education had prepared them well to treat underserved patients and whether this education affected their professional attitudes and behavior concerning providing care for members of historically underserved patient groups. Survey data were collected from 135 residents in U.S. and Canadian graduate orthodontic programs and from 568 active members of the American Association of Orthodontists (AAO). While the majority of residents and orthodontists felt well prepared to treat patients from different ethnic/racial backgrounds (quality of clinical education: residents: 86.4 percent; orthodontists: 82.3 percent), considerably fewer respondents felt well prepared to treat patients on Medicaid (64.7 percent and 34.4 percent), pro bono cases (45.4 percent and 33.4 percent), patients with special needs (52.8 percent and 35 percent), patients with craniofacial anomalies (65.3 percent and 52.6 percent), and patients with developmental delays (45.5 percent and 30.5 percent). Perceptions of the quality of education correlated significantly with the professional attitudes and the actual/projected behavior concerning providing care for patients from these underserved patient groups. Given the lack of access to orthodontic care for patients from underserved patient groups, initiatives are needed to change this situation. These findings showed a clear relationship between how future orthodontists are educated about providing care for patients from underserved populations and their professional attitudes and behavioral intentions to provide care for individuals who historically have encountered access to care barriers. Dental education has to accept the responsibility to prepare future dental care providers to be able to treat patients from underserved groups.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".