"Weighting of the Empathy Questionnaire Toronto Empathy Questionnaire (TEQ) on the Greek Population"
Bibliographic record
Abstract
Empathy is the ability of the therapist to place himself in the position of the treated and to face life and the world through the latter's eyes.According to Coutu (1951), «empathy is the process by which a person momentarily pretends to himself that he is another person…in order that he may get an insight into the other person's probable behaviour in a given situation».Respectively, Goldstein, Michaels (1985), consider that with empathy a person is sensitized to a maximum extent regarding the emotional state of the other and so can go deeper into the individual processes of emotional resonance, cognitive analysis and accurate feedback.It has been found that empathy is an important factor for the development of constructive therapeutic relationship.Goleman (1995) describes empathy as the ability to recognize and understand the desires, goals and views around you.It is a therapeutic tool having its roots in the work of Rogers CR [1], who appointed empathy as the core of person-centred approach to counseling.It is mainly a cognitive characteristic which includes the understanding of the individual's experiences, opinions and perceptions, combining them with the ability for communication and intention to provide assistance.The aim of this study was the weighting of the empathy questionnaire Toronto Empathy Questionnaire on the greek population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".