Higher Confidence Levels in Temporomandibular Disorders Among Predoctoral Students With Clinical Patient Exposure: A National‐Based Cross‐Sectional Study
Bibliographic record
Abstract
OBJECTIVE: The Commission on Dental Accreditation mandated the integration of temporomandibular disorders (TMD) education in predoctoral dental curricula by 2022. However, its impact on student confidence in managing patients with TMD, which is a crucial precursor to implementation, remains unexplored. This study assessed students' confidence levels in TMD-related skills and the influence of different educational approaches. METHODS: An anonymous survey assessing confidence in 12 TMD-related skills (rated 0-10, with 10='the most confident') was distributed to all U.S. predoctoral dental students via the American Student Dental Association in Fall 2024. Confidence levels were compared across academic years, instruction modality (multimodal vs. single-modality) and educational approach (didactic alone vs. didactic + clinical exposure vs. didactic + hands-on/small group discussions) using ANOVA and t-tests. Logistic regression identified predictors of sufficient confidence (> 5 on a 0-10 scale). RESULTS: Responses from 289 participants (26.2 ± 3.8 y/o, 69.3% women) revealed overall low confidence levels (3.5 ± 2.1). Confidence significantly increased with academic year, from 14% of first-year students to 59.4% of fourth-year students reporting sufficient confidence (p < 0.001). Students who received multimodal instruction (p < 0.001), didactic lecture combined with patient exposure (p's between < 0.001 and 0.021) and who had previous clinical exposure (p's between < 0.001 and 0.006) scored significantly higher in confidence than those receiving single-modality approaches, only didactic lecture, and those without patient exposure. Logistic regression identified patient exposure (OR = 2.88, 95% CI 1.11-7.51, p = 0.030) and later academic year (OR = 2.57, 95% CI 1.15-5.71, p = 0.021) as predictors of sufficient confidence. CONCLUSIONS: Despite an overall low confidence in skills related to TMD, confidence levels improved with academic progression, multimodal instruction, and patient exposure.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".