Orthodontic care for underserved patients Professional attitudes and behavior of orthodontic residents and orthodontists
Bibliographic record
Abstract
OBJECTIVES: To explore whether orthodontic residents and orthodontists differ in their attitudes and behavior concerning the treatment of underserved patients and to investigate how background factors such as the providers' gender, ethnicity/race, and age affect these attitudes and behavior. MATERIALS AND METHODS: Survey data were collected from 135 residents in US and Canadian graduate orthodontic programs and from 568 active members of the American Association of Orthodontists. Attitudes toward various aspects of treating underserved patients were rated on a five-point scale, with 1 indicating the most negative attitude and 5 indicating the most positive. RESULTS: Orthodontic residents had more positive attitudes about treating poor patients (3.02 vs 1.99; P < .001), pro bono cases (3.87 vs. 3.45; P < .001), and patients with craniofacial anomalies (3.64 vs 3.01; P < .001) or mental retardation (3.13 vs 2.72; P < .001) than orthodontists. However, compared to orthodontists, lower percentages of residents intended to treat pro bono cases (73.5% vs 83%; P = .009) and patients with craniofacial anomalies (63.6% vs 82.9%; P < .001) or mental retardation (55% vs 81.5%; P < .001). The providers' gender did not have an effect on these attitudes and related behavior, while ethnicity/race and age of the providers were relevant. CONCLUSIONS: Residents had more positive attitudes concerning the treatment of underserved patients than orthodontists. However, their behavioral intentions did not indicate an increased willingness to provide care for these patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".