Investigating the predictive contribution of attitude towards life and belief system on self-resilience and psychological toughness of cancer patients about the mediating role of emotion regulation
Bibliographic record
Abstract
The purpose of the present study was to investigate the predictive contribution of attitude to life and belief system on self-resilience and psychological toughness of cancer patients about the mediating role of emotion regulation. The current research is fundamental in terms of its purpose and descriptive of correlation type in terms of method. The statistical population consists of all cancer patients of Shahinshahr city. The sample size was 150 people based on Cochran’s formula and the country’s corona situation. The sampling method of the current research is available. This research used tools of attitude to life, belief system, self-resilience, psychological toughness, and emotion regulation, all of which had acceptable reliability and validity. The research data was done in two parts, descriptive and inferential, with the help of Excel, SPSS, and SmartPLS software. The results showed that the attitude towards life and the belief system with the role of mediating emotion regulation has a significant predictive ability on the self-resilience of cancer patients. Also, attitude towards life and belief systems with the role of mediating emotion regulation can predict the psychological toughness of cancer patients. In addition, emotional regulation has a significant relationship with self-resilience and psychological toughness. According to the research results, it can be concluded that the attitude towards life and the belief system mediating emotion regulation can predict cancer patients' resilience and psychological toughness. Also, the attitude toward life and the belief system directly impacts. In addition, attitude towards life has a direct positive relationship with psychological toughness.
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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 | 0.000 | 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) | 0.003 | 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".