The Role of Self-compassion and Alexithymia in Predicting Perceived Social Support
Bibliographic record
Abstract
The aim of this study was to examine the relationship between self-compassion and alexithymia in predicting perceived social support. A convenience sample of 181 adults (138 females and 52 males) from Mashhad completed validated measures including the Multidimensional Scale of Perceived Social Support (MSPSS), the Self-Compassion Scale - Short Form (SCS-SF), and the Toronto Alexithymia Scale (FTAS). Data were analysed using Pearson's correlation coefficient and stepwise regression. The results showed that perceived social support was positively correlated with self-compassion and negatively correlated with alexithymia. In addition, self-compassion was found to have a negative and significant relationship with alexithymia. A stepwise regression model with self-compassion and alexithymia as predictors explained 6.5% of the variance in perceived social support. The results indicate that an increase in self-compassion and a decrease in alexithymia lead to an increase in perceived social support. Self-compassion has a greater impact on the prediction of perceived social support. Therefore, individuals who have higher levels of self-compassion and emotional expression tend to perceive higher levels of social support.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".