Predicting Trait Mindfulness by Ego Strength: The Mediating Role of Perfectionism and Alexithymia
Bibliographic record
Abstract
Introduction:In recent years, mindfulness, which has its roots in the Eastern philosophy of Bud� dhism, has made its way into contemporary psychology.Studies have identified two types of mind� fulness: stateful and trait.Studies have shown that these two types of mindfulness are related and that understanding the underlying determinants of one influences understanding the other.This study investigated the predictive role of ego strength in mindfulness among university students, examining perfectionism and alexithymia as mediating variables.Materials and Methods: Using a descriptive-correlational design, 267 students from the University of Kurdistan, Iran, were recruit� ed through convenience sampling.Participants completed validated scales: the Ego Strength Scale (Besharat, 2007), Tehran Multidimensional Perfectionism Scale (Besharat, 2007), Toronto Alex� ithymia Scale (Bagby et al., 1994), and Mindful Attention Awareness Scale (Ghasemi & Ghorbani, 2010).Results: Structural equation modeling demonstrated significant direct and indirect pathways, indicating that ego strength was a significant predictor of perfectionism, alexithymia, and mind� fulness.Both perfectionism and alexithymia directly influenced mindfulness.Mediation analysis showed ego strength indirectly affected mindfulness through both mediators, with specific paths via perfectionism and alexithymia.Conclusion: The dimensions of perfectionism and alexithymia played a significant mediating role in the relationship between ego strength and trait mindfulness.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".