Design of PBL Cases in Bioethical Education Spanning across General and Professional Education
Bibliographic record
Abstract
”Bioethics” is a study of how to face and handle difficulties and dilemma in ”life-related issues”. Bioethics education is featured as a multidisciplinary learning in three aspects, including Population, Behaviors and Life sciences (collectively abbreviated as PBL, similar to problem-based learning as used in this study), those should also be essential learning contents for students in health-care professions. Problem-based learning (PBL) pioneered more than 40 years ago by McMaster University in Canada has proven to be effective in achieving the learning objectives in population, behaviors and life sciences; however PBL may be more successful if the trigger case scenarios are of high quality which can stimulate more interactive and self-directed learning amongst students.A three-level ethical course was proposed at China Medical University to approach gradational learning objectives of a bioethics curriculum. These three levels included; ”Life and ethics”, ”Biomedical ethics” and ”Clinical ethics”. The diverse aims in each level imply the need for multiplicity in case scenarios representing a wide spectrum of ethical issues, such as, cases on the topic of animal right, genetically modified food, stem cell research on human, gene privacy, etc. Cases for the fundamental level were specifically designed to help cultivate an attitude for the respect of life, considering risk vs. benefit, and understanding better in life diversity. For the next level, cases are designed to cultivate the ability of moral reasoning and to realize values in medical profession. These cases include truth telling about end-of-life issues, doctor's duties/right, patient's self determination and research ethics. The ultimate level utilized a narrative approach to several clinical cases based on patient and family's story in order to bring ethical consideration in the clinical setting for professional learning. In general, those cases have been designed to stage effective learning from general to professional learning perspectives.To create effective PBL cases for bioethics learning, the scenarios should lead students to a particular area of study to achieve those diverse learning objects. Ethical problems should not only be appropriate to the level of the students' understanding but also need to infuse sufficient intrinsic interest for students. Scenario should help stimulate discussion and promote self-directed approach in information acquisition from various learning resources. In general, PBL cases consist of ethical enquiries is commonly derived from dilemmas occurring in everyday life, and then evolved into more specific issues relevant to the context in professional practice.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".