The Toronto Empathy Questionnaire: Reliability and Validity in a Nationwide Sample of Greek Teachers
Bibliographic record
Abstract
The present study examined the Toronto Empathy Questionnaire’s (TEQ) validity and reliability in a sample of 3955 Greek teachers. In order to test the internal consistency reliability, the Cronbach’s alpha coefficient was used and was found satisfactory at 0.72. The sample was randomly split and an exploratory factor analysis (EFA) was conducted in the even subsample, justifying the one-factor solution, with the only discrepancy of the low loading of an item. In the odd subsample a confirmatory factor analysis (CFA) was performed to confirm the one-factor model identified by the EFA. The chi square test (χ2) of the model was significant (p < 0.05), while the root mean square error of approximation (RMSEA), the comparative fit index (CFI) and the goodness of fit index (GFI) values were 0.078, 0.969 and 0.960, respectively, further supporting the model’s fit. Student’s t-tests and analysis of variance (ANOVA) showed that women, teachers with children of their own, those working full-time in public schools, those with students who needed special education, and those who had received mental health promotion training, scored higher. Additionally, multiple linear regression analysis revealed that sex, working status, having students who needed special education, and having attended mental health training courses were independently associated with TEQ score. The analyses confirmed that the Greek version of TEQ could be used for researches in Greek educators as a valid and reliable measure of teachers’ empathy.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".