Self-compassion, emotion regulation, and resilience as predictors of psychological well-being in fibromyalgia patients: a cross-sectional study
Bibliographic record
Abstract
While the roles of self-compassion and cognitive emotion regulation in mental health are increasingly acknowledged, their specific impact on fibromyalgia (FM) remains understudied. Given the substantial psychological burden associated with FM, this study aimed to examine these constructs in relation to emotional distress and resilience. Specifically, we sought to: (1) compare self-compassion and emotion regulation strategies between FM patients and healthy controls; (2) explore their associations with depression, anxiety, pain intensity, and resilience; and (3) identify predictors of psychological distress, focusing on self-compassion and emotion regulation. The study included 160 participants (80 FM patients and 80 age- and gender-matched healthy controls) who completed validated instruments, including the Self-Compassion Scale (SCS), Cognitive Emotion Regulation Questionnaire (CERQ), Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Toronto Alexithymia Scale (TAS-20), Brief Resilience Scale (BRS), and Visual Analog Scale (VAS). Group comparisons were conducted using Student's t tests. Pearson correlations assessed associations among psychological variables. Mediation analyses, performed using PROCESS macro with 5000 bootstrap resamples, tested whether resilience mediated the relationships between self-compassion and clinical outcomes. FM patients reported significantly lower self-compassion and greater use of maladaptive emotion regulation strategies-particularly rumination and catastrophizing-compared to healthy controls (p < 0.001). Self-compassion was negatively correlated with depression and anxiety, while resilience was positively associated with self-compassion and inversely related to psychological distress. Regression analyses showed that self-compassion, rumination, catastrophizing, resilience, and pain intensity significantly predicted depression and anxiety. Resilience mediated the relationship between self-compassion and both depressive and anxiety symptoms, though no significant mediation was observed for pain intensity. FM patients experience heightened psychological distress, characterized by reduced self-compassion and increased use of maladaptive emotion regulation strategies. Self-compassion and emotion regulation emerged as key predictors of depression and anxiety, with resilience playing a mediating role in depressive symptoms. These findings underscore the potential of interventions that cultivate self-compassion and strengthen adaptive emotion regulation to improve psychological well-being in individuals with FM and support a more integrative approach to treatment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".