Changes in the components of moral reasoning during students' medical education: a pilot study
Bibliographic record
Abstract
INTRODUCTION: Many authors are concerned by students' moral reasoning not developing normally during medical education. AIM: This study is concerned with how the components of student' moral reasoning are affected by their medical studies. METHODS: Ninety-two medical students were tested on entry into first year and on finishing third year, to determine evolutionary changes in their moral reasoning. Changes in their use of arguments specific to each stage of moral development were measured. RESULTS: Significant changes were observed in the weighted global score (-18.14 +/- 59.17, P = 2.8%). Changes in global score correlated with changes in stages of moral reasoning. The multivariate structure of moral reasoning was reorganised into two principal components, which, respectively, explained almost 82% (first year) and 72% (third year) of the total variability in scores. Moral reasoning stages characterized by law-and-order and social-contract/legalistic orientations proved important for explaining the variability in students' moral reasoning at the start of medical training, while instrumental-relativist and interpersonal-concordance orientations explained variability post third year. CONCLUSIONS: Students restructure their handling of ethical questions by using arguments with more instrumental-relativist and interpersonal-concordance orientations, rather than those of the more desirable law-and-order or social-contract/legalistic type. To assess better the skills required for moral reasoning, a more sophisticated approach is needed than that of a simple measure of improvement/stagnation/deterioration.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".