The Effectiveness of Self-Compassion on the Regulation of Excessive Hostility and Emotional Dysregulation in Mothers with Depressive Symptoms
Bibliographic record
Abstract
Objective: The present study aimed to determine the effectiveness of self-compassion on regulating excessive hostility and emotional dysregulation in mothers with depressive symptoms. Materials and Methods: This research employed a quasi-experimental method with a pretest-posttest design involving an experimental group and a control group. The statistical population included mothers of adolescents enrolled in middle and high school during the 2023–2024 academic year who exhibited depressive symptoms. A convenience sampling method was used to select 30 participants from the population, who were randomly assigned to two groups of 15. The research utilized the Spielberger Anger Questionnaire (1999) and the Toronto Alexithymia Scale (Bagby et al., 1996). The experimental group underwent training based on the self-compassion training protocol. Data were analyzed using univariate and multivariate covariance analysis methods. Findings: The findings indicated that 44% of the individual differences in the anger dimension, 46% in the aggression and insult dimension, and 62% in the stubbornness and resentment dimension in the posttest phase were attributed to differences between groups or treatment effects. Similarly, 40% of individual differences in difficulty identifying emotions, 56% in difficulty describing emotions, and 63% in externally oriented thinking in the posttest phase were related to group differences or treatment effects (p < .05). Conclusion: It can be concluded that self-compassion-based programs can significantly reduce excessive hostility and emotional dysregulation in mothers with depressive symptoms. These findings suggest that enhancing self-compassion can serve as an effective strategy for improving the psychological well-being of mothers suffering from depression and emotional difficulties.
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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 | 0.000 |
| 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".