Emotional intelligence, alexithymia and borderline personality disorder traits in young adults
Bibliographic record
Abstract
Abstract Background A relationship between emotional intelligence (EI) and borderline personality disorder (BPD) has recently been identified. EI has also been found to overlap considerably with alexithymia, a construct associated with emotion processing and emotion regulation. EI and alexithymia may further our understanding of the relationship between emotion processing, emotion regulation and BPD. Aims We examined the relationships between EI, alexithymia and BPD traits, hypothesizing that EI and alexithymia would correlate negatively with each other, that EI would correlate negatively with BPD traits and that alexithymia would correlate positively with BPD traits. We also hypothesized that low EI and high alexithymia would predict BPD traits. Method A sample of 134 male and female university students completed the Bar‐On Emotional Quotient Inventory: Short (EQ‐i:S), the Toronto Alexithymia Scale (TAS‐20) and the Personality Assessment Inventory‐Borderline Scale (PAI‐BOR). Relationships were examined using correlation and multiple regression. Results The EQ‐i:S and the TAS‐20 correlated moderately with each other. None of the EQ‐i:S scales predicted PAI‐BOR. The TAS‐20 total significantly predicted PAI‐BOR. Conclusions The relationship between alexithymia and BPD suggest that difficulty identifying, differentiating, understanding and communicating emotions and feelings (somatic sensations) impairs ability to regulate emotions. It may be that an inability to discriminate emotions and somatic sensations explains why people with BPD who are distressed use deliberate self‐harm as a means to emotion regulation. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".