Unraveling “Feeling Bad” in a Non-Western Culture: Achievement Emotions in Japanese Medical Students
Bibliographic record
Abstract
Introduction: The Medical Emotion Scale has been translated into Japanese (J-MES) and validated for cross-cultural emotion research in medical education. However, its applicability for extracting Japanese cultural aspects of medical students' emotions has not been examined. This study aimed to explore the underlying latent constructs related to culture in the J-MES by conducting factor analyses. Methods: In total, 41 medical students enrolled at a Japanese university participated in this study. The students completed the J-MES before, during, and after a computer-based clinical reasoning activity. Exploratory factor analysis (EFA) was conducted to examine the factor structure of the scale. Factor extraction was based on a scree plot investigation. Results: The EFA for emotions before the task pointed to a four-factor structure explaining 56.70% of the total variance. The first factor accounted for 26.44% of the variance. Based on the seven items with the highest loadings on this factor (e.g., happiness), we interpreted the first factor as representing a positive valence dimension. The second factor explained 13.78% of the variance with four items of highest loadings (e.g., anger), which was interpreted as representing negative emotions toward the learning activity. The third factor explained 10.48% of the variance with three items (e.g., shame), interpreted as negative emotions related to self-performance. The fourth factor explained 6.00% of the variance with three items (e.g., confusion), which was interpreted as representing anxiety-related emotions. Discussion: Negative emotions included multiple factors such as learning activity- and self-performance-related emotions, which could be associated with Japan's interdependent culture. Supplementary Information: The online version contains supplementary material available at 10.1007/s40670-025-02296-w.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".