The relationship between spiritual intelligence and hardiness on the level of pain perception with the mediating role of death Anxiety and Positive Thinking
Bibliographic record
Abstract
Background: Despite years of war, veterans still face many physical and psychological problems that can seriously affect the quality of life of veterans. In the meantime, what is the role of spiritual intelligence and the severity of important variables in the lives of veterans. Aims: The purpose of this study was to investigate the relationship between spiritual intelligence and hardiness on the level of pain perception with the mediating role of death Anxiety and Positive Thinking. Method: The method of this study was correlation with structural equation method. From the statistical population of Kerman province, 280 people were randomly selected and evaluated using the available list. The instruments of the research were Spiritual Intelligence Questionnaire (2008), kobasa (1992), Connor and Davidson (1998), McGill's Pain Perception (2004), Templar’s Death Anxiety Inventory (1998), and Sheer & Carver's Optimism (1985). For data analysis, the path analysis was used in the spss-19 and AMOS software. Results: The results of path analysis showed that after modification of the initial model, the final model had a good fit. In the final model, the ratio of chi-square to relative degrees of freedom or relative chi-square (2.24), goodness of fit index adjusted for 0.92, normalized fit index equal to 0.91 and root mean square error error equal to 05 It was 0. Bootstrap also showed that the mediating role of death anxiety and optimism was significant in the relationship between spiritual intelligence and hardiness with pain perception. Conclusions: spiritual intelligence and hardiness play an important role in decreasing pain perception among veterans by improving optimism and reducing death anxiety
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".