The Effectiveness of Schema Therapy on Post-traumatic Growth and Self-Compassion in Women with Breast Cancer
Bibliographic record
Abstract
Objective: This study aimed to investigate the effectiveness of schema therapy in enhancing post-traumatic growth and self-compassion among women diagnosed with breast cancer. Methods and Materials: The study employed a randomized controlled trial design with two groups: an experimental group receiving schema therapy and a control group receiving no psychological intervention. Thirty women with breast cancer from Tehran were selected through purposive sampling and randomly assigned to each group (15 participants per group). The schema therapy intervention was conducted over ten weekly sessions, each lasting 60 minutes. Participants completed the Post-Traumatic Growth Inventory (PTGI) and the Self-Compassion Scale (SCS) at three time points: pre-test, post-test, and five-month follow-up. Data were analyzed using repeated measures ANOVA and Bonferroni post-hoc tests in SPSS version 27. Findings: The results showed a significant increase in post-traumatic growth and self-compassion in the experimental group compared to the control group across all stages. The experimental group’s mean post-traumatic growth scores improved from 48.73 (SD = 6.21) at pre-test to 67.85 (SD = 5.94) at post-test, while self-compassion scores increased from 62.19 (SD = 5.37) to 78.46 (SD = 5.08). Repeated measures ANOVA confirmed significant time and group interaction effects for both variables (Post-Traumatic Growth: F(2, 56) = 49.33, p < .001, η² = .78; Self-Compassion: F(2, 56) = 46.03, p < .001, η² = .77). Bonferroni post-hoc tests showed that improvements were statistically significant from pre-test to post-test and were sustained at follow-up. Conclusion: Schema therapy is an effective psychological intervention for promoting post-traumatic growth and enhancing self-compassion in women with breast cancer, with lasting effects observed up to five months after the intervention.
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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 | 0.001 |
| 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".