The Relationship Between Psychological Wellbeing & Empathy amongst outstation Indian College Students
Bibliographic record
Abstract
Determining if there's a link between the mental wellness of students who pursue education in various locations and their capacity for empathy can aid organizations and caregivers in providing resilience training and mental health assistance to these young individuals. The research aimed to investigate the relationship between empathy and psychological well-being within a sample of 122 college students from Mumbai, India, who hailed from different regions of the country. (Angeliki Leondari & Gialamas, 2009). This sample comprised 57 male and 67 female students, all aged between 18 and 23 years. The study measures psychological well-being using an 18-item questionnaire divided into six sections: autonomy, environmental mastery, personal growth, positive relationships with others, purpose in life, and self-acceptance. (Franzen et al., 2021). In addition, the empathy quotient is determined by the study using the 16-item Toronto Empathy Scale. The data were statistically examined using inferential statistics, and the Pearson Product Moment Correlation Coefficient was then computed. Consequently, the study showed a strong positive correlation (r=0.295; p<001)between the variables of psychological well-being and empathy. As part of the supplemental observation, the relationship between the different psychological well-being subscale factors was also looked at. students. The study's findings may be used to inform the creation of novel instructional strategies and therapies intended to improve the mental health of distant college students.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".