The Toronto Empathy Questionnaire: Evaluation of Psychometric Properties among Turkish University Students
Bibliographic record
Abstract
Problem statement: Today, it is widely accepted that empathy is a multidimensional factor that facilitates human relations. The common idea that empathy comprises more than one component has created diversity in the assessment of the said factor; many researchers have developed empathy scales that include different dimensions. However, unidimensional assessments minimize differences between assessments and develop an accepted core assessment tool. Purpose of Study: The Toronto Empathy Questionnaire (TEQ) is a selfreport style, uni-dimensional, 16-item, five-point Likert type scale developed to assess the empathy levels of individuals. The objective of this study is to adapt the TEQ into Turkish and to analyze its psychometric properties in a sample of Turkish university students. Methods: Study participants included 698 university students from Ege and Sakarya University. In the research, the Emphatic Tendency Scale and the Basic Empathy Scale were used as data collection tools along with the TEQ. In the adaptation of the questionnaire, a linguistic equivalence study was performed first. The psychometric properties of the TEQ were analyzed through item analysis, exploratory and confirmatory factor
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".