A validation study of the Korean version of the Toronto empathy questionnaire for the measurement of medical students’ empathy
Bibliographic record
Abstract
BACKGROUND: This study aimed to validate the Korean version of the Toronto Empathy Questionnaire (TEQ) and to determine its suitability for the measurement of empathy in medical students. METHODS: The study sample was Year 1 and 2 medical students at two medical schools on six-year undergraduate medical programs in South Korea. The study participants completed the Korean TEQ, which has a single factor structure and consists of 16 items; responses are scored using a 5-point Likert scale, giving a maximum possible score of 64. Psychometric validation of the questionnaire was performed by exploratory and confirmatory factor analyses and the goodness of fit test. Average variance extracted was calculated to establish convergent validity, and associations between factors and construct reliability were analyzed to establish discriminant validity. Cronbach's alpha values were utilized for reliability analysis. RESULTS: A total of 279 students completed and returned the questionnaire (a 96.2% response rate). Participant empathy scores ranged from 20 to 60 (M = 44.6, SD = 7.36). Empathy scores were higher for females than males (p < .05). The cumulative variance of the Korean TEQ was 32%, indicating that its explanatory power was rather weak. Consequently, goodness-of-fit testing was performed on four hypothetical models, among which a three-factorial structure consisting of 14 items demonstrated satisfactory fit indices and explained 55% of the variance. Reliability estimates of the three subscales were also satisfactory (Cronbach's α = .71-.81). This three-factorial model was validated by confirmatory factor analysis and demonstrated adequate convergent and discriminant validity. CONCLUSIONS: This study demonstrated psychometric validation of the Korean TEQ for measuring medical students' empathy. We suggest a modified 14-item model with a three-factorial structure, which demonstrated better psychometric properties than the original scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".