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Interplay between Empathy and Pro-Social Behavior among Undergraduate University Students

2024· article· en· W4399081902 on OpenAlexaboutno aff
Muqadas Shafique, Syeda Sajida Firdos, Maham Imtiaz

Bibliographic record

VenuePakistan Journal of Humanities and Social Sciences · 2024
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsnot available
Fundersnot available
KeywordsEmpathyPsychologySocial psychologyMathematics education

Abstract

fetched live from OpenAlex

This quantitative study examined the interplay between empathy and prosocial behavior among undergraduate university students. A sample of (N=220) undergraduates from 11 public and private universities of Punjab, Pakistan participated through the online survey. These undergraduate students (ages between 18 to 25 years), were chosen from diverse academic disciplines of selected universities. Prosocial behavior was measured using the Prosocial Behavior Scale (Caprara et al., 2005), and Toronto Empathy Questionnaire (Spreng et al., 2009) was utilized to measure empathy. The study employed convenience sampling technique and utilized SPSS (version 25) for data analysis. Results indicated a significant positive correlation (r =.453**) between prosocial behavior and empathy among undergraduate university students, which means that higher empathy will aid escalation in prosocial behavior. Furthermore, significant gender difference (p=.002**) was found in the level of empathy, female students exhibited higher level of empathy as compared to male students. Conversely, no gender difference was found in the level of pro-social behaviors. moreover, students belonging to the urban areas showed significantly greater level (p=.001**) of pro-social behavior as compared to rural student, while the insignificant difference was seen regarding level of empathy among urban and rural students. These findings will contribute to induce the empathy and pro-social behaviors to create a sustainable society.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.373
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venuePakistan Journal of Humanities and Social SciencesSame topicBullying, Victimization, and AggressionFrench-language works237,207