The “Hot Seat” Experience: A Multifaceted Approach to the Teaching of Ethics in a Dental Curriculum
Bibliographic record
Abstract
The subject of ethics and the teaching of skills associated with ethical reasoning in a predoctoral dental curriculum are as important as clinical skills development, but there is no single approach to teaching ethics in dentistry. This article aims to describe the didactic approach used to teach dental ethics and ethical reasoning in the first year of the D.M.D. curriculum at the University of British Columbia. This descriptive article discusses three main pedagogies employed to teach ethics: the "hot seat" experience via a role-play with a trained actor (standardized patient, SP); small-group presentations of a case workup deconstructing an ethical dilemma; and student reflections from the SP encounters. The approach to dental ethics presented here does not profess to make an otherwise unethical person ethical, but it can give all students the tools to recognize when a dilemma exists, use a process to reason ethically, and ultimately make a good decision. The "hot seat" and the case workup approaches have had a positive impact upon students as illustrated through their reflections; however, further study is needed to better understand the implications of ethical issues in both academic and professional settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| 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".