Turkish Adaptation of Counter-Empathy Scale and its Psychometric Properties
Bibliographic record
Abstract
Objective: This study aims to adapt the Counter-Empathy Scale, which assesses counter-empathy, a new concept in the literature, to Turkish and to examine its psychometric properties. Method: The study was conducted with a community sample of 347 people, 209 (60.2%) of whom were women, aged 18-66 (34.04±12.66), reached through convenience and snowball sampling methods. Counter-Empathy Scale (CES), Toronto Empathy Scale (TES), Adult Prosocialness Scale (APS), Horney-Coolidge Tridimensional Inventory (HCTI), and Existential Anger Scale (EAS) were applied to the participants. Results: As a result of the confirmatory factor analysis conducted for the validity of the scale, it was determined that the goodness of fit index values (X2/df=3.21, AGFI=0.89, GFI=0.91, CFI=0.92) were at an acceptable level, and the 2-factor model proposed in the original study was confirmed. In addition, it was observed that the factor loadings of all items in the scale varied between .55 and .81. Statistically significant results were obtained in the correlation analyses conducted for convergent validity. It was also determined that the scale could distinguish groups with a history of psychiatric or psychological help from groups without at a significant level. It was determined that the Cronbach Alpha reliability coefficients of the scale were .87 for the entire scale, .84 for the dimension of taking pleasure in others’ pain, and .86 for the sub-dimension of feeling annoyed with others’ happiness. The item-total correlations of all items were above .50. It was observed that the test-retest correlation coefficients were between .71 and .86. Conclusion: The results obtained from all analyses showed that the Counter-Empathy Scale is a valid and reliable scale with strong psychometric properties and is a measurement tool that can be used in research conducted in Turkish culture and clinical settings.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".