The Mediating Role of Hope with Mindfulness: Empathy
Bibliographic record
Abstract
It is an important characteristic that individuals do not become hopeless in the face of adversity but instead strive with determination to achieve their goals and look forward to the future with hope. Hopelessness can lead to negative outcomes in people's lives. It is also a positive psychological indicator that people can empathize with others. Individuals can become aware of the feelings and thoughts of others and understand events from their perspective. Individuals may miss many moments while going about their daily lives. In this case, the concept of mindfulness, which expresses people's focus on the present moment, comes to the forefront. In this context, the current study seeks to investigate the role of empathy in mediating the concepts of mindfulness and hope. The relationships between these variables have never been examined before, and they are addressed for the first time in the current study. The data was collected with the voluntary participation of 139 male and 694 female participants. The Mindful Attention Awareness Scale, Toronto Empathy Scale, and Persevering Hope Scale were used as measurement tools during data collection. Structural Equation Modeling (SEM) was used to conduct mediation analysis. According to the findings, empathy plays a crucial role in mediating mindfulness and hope. In other words, mindfulness predicts hope indirectly via empathy. According to the study's findings, people who practice mindfulness may have higher levels of empathy and hope. In this regard, mindfulness-based programs are thought to be effective in bringing both improved empathy and more hopeful individuals into society.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".