Emotional awareness in adolescents with a borderline personality disorder: impact of defense mechanisms and adversity
Bibliographic record
Abstract
Background: In Borderline Personality Disorder (BPD), the literature on emotion awareness revealed some discrepancies between some patients who are accurate in detecting emotional cues and others that are impaired at identifying them in themselves and others.Objective: Even though some links between childhood adversity and emotion regulation have been established, there is a need to understand the mechanisms underlying this association, and defense mechanisms, which are central to emotion regulation, and could play a key role in this pathway.Method: The purpose of this study is to compare levels of emotional awareness between adolescents with BPD and healthy controls and to investigate the influence of adversity (maltreatment and parental bonding) and defense mechanisms influence on emotional awareness in the two groups, as measured by the Level of Emotional Awareness Scale (LEAS). The study includes BPD adolescents (n = 41) and healthy controls (HC, n = 67).Results: No difference was observed between the LEAS in BPD and HC, but multivariate regression models analysed highlighted links between levels of emotional awareness and adversity: low levels of self-emotion awareness were associated with maltreatment and with maternal control, while high levels of emotional awareness were linked to low levels of maternal care.Conclusions: In BPD, immature defense mechanisms mediated the association between maltreatment and self-emotional awareness, suggesting a key role of defense mechanisms in maintaining a normal level of emotional awareness in the face of adversity.
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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 | 0.000 | 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".