Medical students' perspectives on the ethics of clinical reality.
Bibliographic record
Abstract
INTRODUCTION: Medical ethicists have pointed out that a gap exists between classroom teaching of bioethical theory and the ethics of clinical reality. Studies recommend that the teaching of bioethics should focus on everyday dilemmas in the clinical setting instead of only dramatic dilemmas and have expressed the need for more studies of how medical students perceive ethical problems in the clinical setting. This study explored themes in and types of ethical dilemmas in medical students' reflective writing in their clinical rotations. METHODS: The study was a qualitative explorative analysis of group reflection texts from fourth-year medical students at Aarhus University, Denmark. RESULTS: The thematic analysis of 51 group reflection texts (n = 396) revealed four key themes in the material: 1) confidentiality issues, 2) treatment options and side effects, 3) the students' role and responsibility and 4) information-giving and communication. The majority of the ethical dilemmas that the students identified were everyday dilemmas. Dramatic dilemmas were represented to a limited degree. CONCLUSIONS: Students' perspectives on ethical dilemmas in the clinical setting provide a unique opportunity to integrate a variety of ethical dimensions into bioethical education and draw attention to overlooked everyday ethical dilemmas. Thus, involving the students' perspectives may be a way to bridge the gap between bioethical theory and the ethics of clinical reality. FUNDING: none. TRIAL REGISTRATION: not relevant.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| 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".