Assessment of Graduate Orthodontic Programs in North America
Bibliographic record
Abstract
This study collected information on four main aspects of U.S. and Canadian orthodontic programs: demographic profiles of residents, requirements for graduation, graduate curriculum, and number of faculty and staff members. Program directors at seventy U.S. and Canadian orthodontic programs were invited to participate in a twenty-question survey and to distribute a ten-question survey to their residents. Twenty program directors and eighty-four residents completed the anonymous, online surveys on Qualtrics.com in July-August 2010. The average age of surveyed residents was 29.6 years of age; 73 percent were non-Hispanic white, with 14 percent Asian/Asian-American, 5 percent Hispanic, and 1 percent African American. A small percentage of residents (13 percent) were foreign-trained. The majority of residents (64 percent) were male. There was a wide variety of clinical and didactic requirements in the programs. Almost all programs emphasized treatment with functional appliances and clear aligners. An average of three full-time and ten part-time faculty members were dedicated to each residency program. This survey reveals a potential shortage of minority orthodontic residents currently being trained in orthodontic programs, in addition to several commonalities and differences among the programs' curricula, graduation requirements, and numbers of faculty and staff members. This preliminary survey will hopefully inspire measures to address the discrepancies revealed, particularly the lack of minority students and full-time faculty members.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".