Forgiveness and Empathy as Predictors of Psychological Wellbeing among Nurses
Bibliographic record
Abstract
The purpose of the study was to examine whether forgiveness and empathy have a significant correlation with psychological wellbeing among nurses. The present research will enable the theorist to develop measures that will cultivate empathy, forgiveness and psychological wellbeing in nurses. This was a correlational, survey based research. A sample of 151 nurses of age range 20-55 was selected via purposive sampling technique from Karachi, Pakistan. The participants were approached through online medium. The study variables were assessed through Toronto Empathy Questionnaire (Spreng et al., 2009), The Heartland Forgiveness scale (Thompson et al., 2005) and Ryff’s Psychological Well-Being Scales (Ryff, 1989). Statistical Package for Social Sciences (SPSS- Version 22) was used for analyzing the data, the results revealed that empathy and forgiveness are significant predictors of psychological wellbeing. Further findings of the research highlighted significant differences in empathy, forgiveness and psychological wellbeing with respect to nurse’s age, gender, education and family structure. The results of the present study could be useful for enhancing nurse’s psychological.
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".