Some Paradoxes in Competency‐Based Dental Education
Bibliographic record
Abstract
Competency-based dental education was introduced in 1993 and has proven to be a robust innovation, guiding curricular design, clinical education and evaluation, and accreditation. At the same time, it has been irregularly implemented and is understood in different ways. These paradoxes were explored in a survey of academic and clinical deans and chairs of departments of endodontics and restorative dentistry at U.S. and Canadian dental schools. It was confirmed that fewer than half of the respondents can identify the ADEA and ADA definition of competency. Significant differences were reported in the perceived understanding and value placed on competencies and their impact on dental education. Differences were also found to exist in evaluation practices and in how evaluation data are used to determine students' readiness for graduation. It is concluded that the openness of the competency concept is one reason for its longevity and usefulness in dental education.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".