Social support and self-compassion mediate the relationship between alexithymia and quality of life in postoperative breast cancer patients: a cross-sectional study
Bibliographic record
Abstract
Background: In breast cancer patients, alexithymia has been found to correlate with poorer quality of life. While previous research has established a connection between alexithymia and various outcomes, the mediating effect of social support and self-compassion-promoting quality of life-remains largely unexplored, underscoring the need for further investigation in this area. Objective: To examine the mediating role of social support and self-compassion in the association between alexithymia and quality of life in postoperative breast cancer patients. Methods: A cross-sectional correlational study was conducted among 324 postoperative breast cancer patients from a tertiary Grade A hospital in Guangzhou, China. Variables were measured using the Functional Assessment of Cancer Therapy-Breast version 4.0 (FACT-Bv4.0), Toronto Alexithymia Scale (TAS-20), Social Support Rating Scale (SSRS), and Self-Compassion Scale (SCS). Data analyses were performed using descriptive analysis, independent sample t-tests, one-way ANOVA, Pearson correlation analysis, and mediation analyses performed with Hayes' PROCESS macro for SPSS. Results: The study identified alexithymia was negatively associated with quality of life. Additionally, social support and self-compassion mediated the relationship between alexithymia and quality of life in postoperative breast cancer patients. Conclusion: The study highlights the complex interplay between alexithymia, quality of life, social support and self-compassion, emphasizing the significant mediating effects of social support and self-compassion among breast cancer patients. Additionally, the findings imply that interventions targeted at enhancing social support and self-compassion could manage the consequences of alexithymia, and ultimately improve their quality of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".