Illness appraisal mediates the relationship between gratitude and quality of life among cancer patients
Bibliographic record
Abstract
Introduction:The level of gratitude may explain an increase in quality of life.Illness appraisal is a valid factor that modifies coping with stress of cancer.The aim of the research was to investigate the role of illness appraisal and gratitude for the quality of life of cancer patients.It was hypothesized that appraisal of the disease mediates the relationship between gratitude and quality of life in oncological patients with a moderating effect of gender. Material and methods:The participants comprised 96 Polish cancer patients, with breast or prostate cancer, hospitalized during 5-7 weeks of radiotherapy, and aged 31-79 years.A gratitude questionnaire, the disease-related appraisals scale, and the sense of quality of life questionnaire were used.Results: The appraisal of the disease in the category of harm was a variable mediating the relationship between the sense of gratitude in the all measured dimensions of quality of life: global, psychophysical, psychosocial, subjective and metaphysical.Mediation moderated by gender occurred in the relationship between gratitude and the metaphysical dimension of quality of life, and the appraisal of the disease as harm serves as a variable mediating this relationship.Conclusions: The mediating variable in the relationship between gratitude and the metaphysical dimension of quality of life was the appraisal of illness as harm.The above mediation relationship turned out to be moderated by gender.The appraisal of the disease in the category of harm turned out to be a variable mediating the influence of gratitude on the metaphysical quality of life only in women.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".