Alexitimia e depressão : a influência mediadora da autocompaixão e mindfulness
Bibliographic record
Abstract
Alexithymia is characterized by difficulty in identifying and describing emotions, while depression manifests as persistent feelings of sadness and apathy. In this context, mindfulness and self-compassion emerge as psychological processes that can moderate the impact of alexithymia on depression, favoring emotional regulation. The present study aimed to investigate the relationship between alexithymia and depression, exploring the mediating role of mindfulness facets and self-compassion factors. To this end, a cross-sectional observational method was used with 145 university students, who responded to a sociodemographic questionnaire and four validated scales in Portuguese: the Five Facet Mindfulness Questionnaire, the Self-Compassion Scale, the Toronto Alexithymia Scale, and the Depression, Anxiety, and Stress Scale. The average age of participants was 25 years, predominantly female and residing in João Pessoa. Mediation analyses revealed that both mindfulness and self-compassion played significant mediating roles in the relationship between alexithymia and depression. Specifically, in the mindfulness model, the facets "Acting with Awareness" and "Non-Judging" were identified as crucial mediators in reducing depressive symptoms. In contrast, in the self-compassion model, the factors "Over-Identification" and "Isolation" emerged as risks, associated with increased depressive symptoms. These findings suggest that interventions focused on developing mindfulness and self-compassion may be effective in emotional regulation and in the treatment of depression in individuals with alexithymia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".