Early Clinical Exposure in U.S. Dental Schools and Correlation with Earlier Competencies Evaluation
Bibliographic record
Abstract
Abstract Early clinical exposure (ECE), defined as any interaction with patients prior to the portion of the curriculum when den‐ tal students spend most of their time at school as a primary provider, is a growing trend in curriculum reform across U.S. dental schools in the 21st century. The aims of this study were to characterize the types of ECE implementation in U.S. dental schools and determine if ECE correlated with earlier clinical competency assessments. In September 2018, the academic deans of all 66 U.S. dental schools were invited to respond to an eight‐item electronic survey about ECE at their schools. Representatives of 40 schools submitted complete responses, for a response rate of 60.6%. Among the respondents, 85% reported their schools started their principal clinical experience (PCE), the portion of the curriculum when students spend most of their time as the primary provider for patients, during the last quarter of Year 2 or the first quarter of Year 3. Respondents at all 40 schools reported offering some form of ECE as part of the formal curriculum, with shadowing and performing dental prophylaxis the most commonly of‐ fered types. No statistically significant associations were found between specific types of ECE and related Commission on Dental Accreditation (CODA) clinical standards for both formative and summative assessments. Although U.S. dental schools have been incorporating more ECE into their curricula over the past decade, these findings suggest that it has not led to earlier clinical competency assessments.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".