Validation of the Romanian Version of the Toronto Empathy Questionnaire (TEQ) among Undergraduate Medical Students
Bibliographic record
Abstract
Medical professionals require adequate abilities to identify others’ emotions and express personal emotions. We aimed to determine the validity and reliability of an empathy measuring tool in medical students for this study. We employed Spreng’s Toronto Empathy Questionnaire (TEQ) as a starting point for this validation. The process was performed in several steps, including an English-Romanian-English translation and a focus group meeting to establish each question’s degree of understandability and usability, with minor improvements of wording in each step. We checked internal and external consistency in a pilot group (n = 67). For construct and convergent validity, we used a sample of 649 students. The overall internal and external reliability performed well, with Cronbach’s alpha = 0.727 and respective ICC = 0.776. The principal component analysis resulted in 3 components: prosocial helping behavior, inappropriate sensitivity, dismissive attitude. Component 1 includes positively worded questions, and components 2 and 3 include negatively worded questions. Women had significantly higher scores than men in convergent validity, but we did not highlight any differences for other demographic factors. The Romanian version of the TEQ is a reliable and valid tool to measure empathy among undergraduate medical students that may be further used in subsequent research.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".