Dental Education About Patients with Special Needs: A Survey of U.S. and Canadian Dental Schools
Bibliographic record
Abstract
The objectives of this study were to explore how U.S. and Canadian dental schools educate students about special needs patients and which challenges and intentions for curricular changes they perceive. Data were collected from twenty-two dental schools in the United States and Canada with a web-based survey. While 91 percent of the programs covered this topic in their clinical education, only 64 percent offered a separate course about special needs patients. The clinical education varied widely. Thirty-seven percent of the responding schools had a special clinical area in their school for treating these patients. These areas had between three and twenty-two chairs and were funded and staffed quite differently. Most programs covered the treatment of patients with more prevalent impairments such as Down syndrome (91 percent), autism spectrum disorders (91 percent), and motion impairments (86 percent). Written exams were the most common outcome assessments (91 percent), while objective structured clinical examinations (18 percent) and standardized patient experiences (9 percent) were used less frequently. The most commonly reported challenge was curriculum overload (55 percent). The majority (77 percent) planned educational changes over the next three years, with 36 percent of schools planning to increase clinical and 27 percent extramural experiences. The findings showed that the responding U.S. and Canadian dental schools had a wide range of approaches to educating predoctoral students about treating special needs patients. In order to eliminate oral health disparities and access to care issues for these patients, future research should focus on developing best practices for educational efforts in this context.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".