Evolution of Clinical Reasoning in Dental Education
Bibliographic record
Abstract
The approach to care in dentistry has evolved over the past couple of decades from a narrow focus on oral disease to addressing the psychosocial determinants of oral health. Subsequently, there have been many attempts to reform dental curricula through alternative models of education, such as competency-based and community-based educational models and problem-based learning. These efforts aim to improve the abilities of dental students in problem-solving, critical thinking, professionalism, and social and cultural competence to help them cope with the complexity of dealing with oral health-related issues and the constantly changing evidence underlying the practice of dentistry. However, it is not yet clear how well these educational initiatives meet their objectives or how they influence the reasoning skills of dental students. There is now a need to develop a conceptual framework for clinical reasoning in dentistry grounded on empirical evidence to direct the future evolution of dental education.
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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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".