Alexithymia, Self-Compassion, Emotional Resilience, and Cognitive Emotion Regulation: Charting the Emotional Journey of Cancer Patients
Bibliographic record
Abstract
Cancer’s profound impact on emotional well-being necessitates an exploration into the underlying psychological mechanisms influencing depression and anxiety in patients. In this study, we explored the potential role of self-compassion, alexithymia, and cognitive emotion regulation mechanisms in influencing depressive and anxiety symptoms among cancer patients. A total of 151 stage 4 cancer patients participated. Instruments applied included the Beck Depression Scale (BDS), Beck Anxiety Inventory (BAI), Self-Compassion Scale (SCS), Cognitive Emotion Regulation Scale (CERQ), Toronto Alexithymia Scale (TAS), Visual Analogue Scale (VAS), and Brief Psychological Resilience Scale (BRS). The multivariate analysis utilizing the independent variables—SCS, adaptive and maladaptive CERQ, TAS subscales, BRS, and VAS scores—accounted for 39% of the variance seen in BDI (F (8142) = 11.539, p < 0.001). Notably, SCS, adaptive CERQ, and BRS had a negative predictive impact on BDI. Our findings substantiate a statistically significant partial mediatory role of resilience and cognitive emotion regulation in the association between self-compassion and depression. This research accentuates the central role self-compassion, emotional resilience, and cognitive regulation play in the emotional well-being of individuals diagnosed with cancer. Targeted therapeutic interventions focusing on these dimensions may enhance the psychological health of patients, ultimately improving overall treatment outcomes in the oncological setting.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".