What Health Science Students Learn from Playing a Standardized Patient in an Ethics Course
Bibliographic record
Abstract
Formal teaching of ethics in health science programs at the entry level and postprofessional level in the United States and Canada has been documented in the professional literature for more than 30 years, yet there are significant differences in the way it is taught and how much time is devoted to the subject. Numerous teaching and evaluation methods have been used in ethics education, such as lectures, written examinations, debates, role-playing, small group discussion, and case study analysis. Most instruction in ethics in the health sciences has been geared toward ethical analysis of case studies, that is, the student is asked to read a case or discuss a case with others, identify the ethical issues verbally or in writing, propose different resolutions supported by principles and theory, and select the best course of action. Yet, analysis of a case is an unlikely route to develop skills in coping with the uncertainty and emotional nature of ethical issues commonly encountered in clinical practice, nor does it give us an indication of what students would “really do” when they encounter an actual ethical problem.
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 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 | 0.069 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.062 |
| 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; both teacher heads agree on what is shown here.
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".