The Relationship of Mindfulness, Self-Differentiation and Alexithymia With Borderline Personality Traits
Bibliographic record
Abstract
Background: Psychological problems such as borderline personality traits can negatively affect students' behaviors, cognition, interpersonal communication and academic achievement. It is important to identify factors such as mindfulness, self-differentiation, and alexithymia and determine their relationship these traits. Objectives: This study was performed to investigate the relationship of mindfulness, self-differentiation and alexithymia with borderline personality traits. Methods: In this descriptive correlational study, 309 students from Shahid Bahonar University of Kerman (217 female and 92 male) were selected using the random cluster sampling method. They completed the Five Factor Mindfulness Questionnaire, the Self-Differentiation Scale, the Toronto Alexithymia Scale and the Borderline Personality Scale. After collecting the questionnaires, the data were analyzed using SPSS-24 and AMOS-24 software programs and path analysis method. Results: The analyses showed that the direct effect of mindfulness was significant only on fear of intimacy (p <0.05). Self-differentiation predicted three sub-scales of borderline personality including defense mechanisms, fear of intimacy (p <0.001) and reality testing (p <0.05) in a significant and negative manner. Alexithymia had a significant positive impact on all subscales of borderline personality including identity disturbance, primary defense mechanisms, fear of intimacy (p <0.001) and damaged reality testing (p <0.05). Conclusion: Alexithymia, self-differentiation and mindfulness were the most powerful predictors of students' borderline personality traits, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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".