The Role of Self-Compassion in Buffering Symptoms of Depression in the General Population
Bibliographic record
Abstract
Self-compassion, typically operationalized as the total score of the Self-Compassion Scale (SCS; Neff, 2003b), has been shown to be related to increased psychological well-being and lower depression in students of the social sciences, users of psychology websites and psychotherapy patients. The current study builds on the existing literature by examining the link between self-compassion and depressive symptomatology in a sample representative of the German general population (n = 2,404). The SCS subscales of self-judgment, isolation, and over-identification, and the "self-coldness", composite score, which encompass these three negative subscales, consistently differed between subsamples of individuals without any depressive symptoms, with any depressive syndromes, and with major depressive disorder. The contribution of the positive SCS subscales of self-kindness, common humanity, and mindfulness to the variance in depressive symptomatology was almost negligible. However, when combined to a "self-compassion composite", the positive SCS subscales significantly moderated the relationship between "self-coldness" and depressive symptoms in the general population. This speaks for self-compassion having the potential to buffer self-coldness related to depression--providing an argument for interventions that foster self-caring, kind, and forgiving attitudes towards oneself.
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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".