Self-Compassion, Emotional Intelligence and Empathy Among Private University Teachers
Bibliographic record
Abstract
The purpose of this study was to investigate the relationships between self-compassion, emotional intelligence, and empathy among university Teachers.The study's participants included both male and female university instructors (N = 155) from Lahore, Pakistan with age range 25 to50 years (M = 33.7,SD = 5.2).The purposive sample technique was used to select teachers from private universities.Variables have been assessed with the help of The Self-Compassion Scale-Short Form (SCS-SF), The Schutte Self-Report Emotional Intelligence (SSEIT), The Toronto Empathy Survey (TEQ).The correlation analysis showed positive correlation among self-compassion, emotional intelligence and empathy in teachers.Regression analysis showed that selfcompassion and emotional intelligence are significant predictors of empathy among university teachers.Results revealed significant differences for man and woman regarding selfcompassion, emotional intelligence and empathy.Results showed a strong positive association between self-compassion, emotional intelligence, and empathy.In university professors, selfcompassion and emotional intelligence were highly significant positive predictors of empathy.The findings showed that there were higher emotional intelligence and empathy scores were seen among female university professors. Keyword. Self-
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".