Development of the Empathy Ability Questionnaire in Intimate Relationships among College Students
Bibliographic record
Abstract
Empathy ability is the ability to share and understand others' feelings and experiences. It is how a person feels when they watch or imagine how someone else feels. Close relationships are relationships where people depend on each other a lot. They include many kinds, such as romantic relationships, marriage, family ties, classmate relationships and friendships. Empathy ability is a key thing in close relationships. We combined empathy ability with close relationships. We made a questionnaire called "Empathy Ability in Close Relationships Questionnaire". We wanted to find out if college students' empathy ability is different when they face different close relationships. We collected study data in three ways: looking at many papers, asking experts to judge and discuss, and giving out the questionnaire. We used some statistical methods, like exploratory factor analysis, independent - samples t - test and repeated measures analysis of variance. We also used the Toronto Empathy Questionnaire to test the effect. We wanted to know how good our questionnaire was and test its reliability and validity. Finally, we made the "College Students' Empathy Ability in Close Relationships Questionnaire". It has 22 questions. It includes 3 parts: family empathy, friend empathy and lover empathy. The Cronbach's alpha coefficient is 0.896. The split - half reliability is 0.918. The results show two things. First, girls' empathy ability is much stronger than boys'. Second, empathy ability is very different in the three parts: family, friendship and love. College students have the highest empathy ability in friendship. They have the lowest empathy ability in family relationships. In short, this questionnaire has good reliability and validity. It meets the standards of psychometrics. It can be used as a tool to measure college students' empathy ability in close relationships.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".