Professional Quality of Life, Empathy, and Coping Strategies of Young Clinical Psychologists in Lahore, Pakistan
Bibliographic record
Abstract
Objectives Empathy, coping strategies, and professional quality of life (ProQoL) play a significant role in an individual’s professional life. Several studies have indicated that the unfavorable consequences of compassion and empathy lead to compassion fatigue and burnout. The present study aims to examine the correlation among empathy, coping strategies, and ProQoL, and assess the moderating role of work experience among young clinical psychologists in Lahore, Pakistan. Methods This is a descriptive-correlational survey. A purposive sampling technique was used to select participants aged 23-37 years with 1-10 years of work experience. Data was collected using standard questionnaires including the Toronto Empathy Questionnaire, the Brief COPE Inventory, and the ProQoL Scale. Their internal consistency was examined using Cronbach’s alpha. Data were analyzed using descriptive statistics, correlational test, categorical regression analysis, and moderation analysis. Results There was a strong correlation between empathy, adaptive coping strategies, and compassion satisfaction. Moderation analysis showed that empathy and years of work experience significantly predicted the ProQoL. Conclusion Clinical psychologists in Lahore need to be educated to stimulate and further use problem-focused and adaptive coping skills to cope with their challenging jobs.
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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 | 0.001 |
| 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) | 0.001 | 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".