The Role of Empathy and Vicarious Trauma on Wisdom and Psychological Distress among Lawyers
Bibliographic record
Abstract
Background: The present study aims to understand the effects of Empathy and Vicarious Trauma on Wisdom and Psychological Distress among lawyers.In India, the ratio of lawyers to the entire population is quite low and hence, lawyers often find themselves overworked.Constantly working in a stressful environment such as a court has a negative impact on mental health.Therefore, understanding their mental health is crucial.Methodology: A quasi-experimental design was used in the study.The data was collected from 94 litigating civil and criminal lawyers from age of 24 -50 years (females = 54; males = 40).The sample was derived by the Purposive sampling method.The Toronto Empathy Scale, The Vicarious Trauma Scale, The Threedimensional Wisdom Scale-12, and The Kessler Psychological Distress Scale were administered to measure Empathy, Vicarious Trauma, Wisdom, and Psychological Distress respectively.Results: As a statistical tool for data analysis, Independent Samples t-tests were used.The results indicated that Empathy has a significant effect on Wisdom [ t (92) = 3.48, p<0.01].However, Empathy has no significant effect on Psychological Distress [t (92) = 0.23, ns].Furthermore, Vicarious Trauma has a significant effect on both Wisdom [ t (92) = 2.31, p<0.05] and Psychological Distress [t (92) = 3.56, p<0.01]. Conclusion:In India, the mental health of lawyers is often compromised which has serious repercussions.Hence, there is a need to address these psychological concerns for the psychological well-being of the lawyers.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".