The effectiveness of mindfulness-based cognitive therapy on self-compassion, Alexithymia and cognitive distortion of students experiencing love failure.
Bibliographic record
Abstract
The purpose of this study was to investigate the effectiveness of mindfulness-based cognitive therapy on self-compassion, Alexithymia, and cognitive distortion of students with Love Trauma Syndrome. The statistical population of this research includes women students with love trauma syndrome in Azad University Qazvin who were studying in 1399-1400. The sample of the present study was selected through a call and voluntarily from female students who experienced love failure in their lives and People who got a score above 20. People selected the final sample and then these people were randomly assigned to the experimental and control groups. In this study, to collect data, self-compassion Neff (2003) questionnaire, cognitive distortion Elis (1998) questionnaire, and Alexithymia Toronto (1994) questionnaire were used. Also in this study, multivariate analysis of covariance was used to analyze the data. The results of data analysis showed that mindfulness-based cognitive therapy training had a significant effect on self-compassion, Alexithymia, and cognitive distortions of women students with Love Trauma Syndrome (p <0.001). The results of the study showed that counselors and psychologists can use mindfulness-based cognitive therapy to increase their compassion, reduce emotional distress, and cognitive distortion in students with Love Trauma Syndrome.
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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 | 0.000 | 0.001 |
| 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".