Is problem‐based learning an ideal format for developing ethical decision skills?
Bibliographic record
Abstract
Ethical decision making is a complex process, which involves the interaction of knowledge, skills, and attitude. To enhance the teaching and learning on ethics reasoning, multiple teaching strategies have to be applied. A medical ethical reasoning (MER) model served as a framework of the development of ethics reasoning and their suggested instructional strategies. Problem-based learning (PBL), being used to facilitate students' critical thinking, self-directed learning, collaboration, and communication skills, has been considered effective on ethics education, especially when incorporated with experiential experience. Unlike lecturing that mainly disseminates knowledge and activates the left brain, PBL encourages "whole-brain" learning. However, PBL has several disadvantages, such as its inefficiency, lack of adequately trained preceptors, and the in-depth, silo learning within a relatively small number of cases. Because each school tends to utilize PBL in different ways, either the curriculum designer or the learning strategy, it is important to maximize the advantages of a PBL session, PBL then becomes an ideal format for refining students' ethical decisions and behaviors.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".