Dentists’ ethical practical knowledge: a critical issue for dental education
Bibliographic record
Abstract
Dentists, just like teachers, when moving from school to the workplace (dental offices and classrooms respectively), may find it difficult to apply theories learned during formal education to the complexities of the workplace environment. These difficulties constitute a critical issue for dental education in the area of ethics. In teacher education, the knowledge teachers develop for dealing with the situations they encounter in the classroom is called teachers' practical knowledge. In this study, I discuss the concept of teachers' practical knowledge within the context of dentistry, focusing on the ethical dimensions of dentistry practice, arguing for a dentists' ethical practical knowledge. In this sense, I articulate the similarities between teachers' practical knowledge and its equivalent in dental education, suggesting alternatives for dental education that could foster the development of dentists' ethical practical knowledge.
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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 | 0.078 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.073 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.021 | 0.029 |
| 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".