Emotion word repertoire in the adult attachment interview predicts a reduction of non-suicidal self-injury in the psychotherapy of borderline personality disorder
Bibliographic record
Abstract
Borderline personality disorder (BPD) is characterized by mental representation deficits and emotion dysregulation, with non-suicidal self-injury (NSSI) often occurring as a maladaptive regulation strategy. The ability to verbally express emotions might be advantageous for coping with emotion dysregulation and benefiting from psychotherapy. In the present study, we used a novel text-based measure of emotional awareness to examine whether a greater emotion word repertoire (EWR) predicts improvement in psychotherapy for patients with BPD regarding NSSI, suicide attempts, attachment representations, mentalization, and personality organization. We conducted a secondary analysis of a randomized controlled trial comparing the efficacy of Transference-Focused Psychotherapy (TFP) vs. treatment as usual over one year in a sample of female BPD outpatients. The German electronic Levels of Emotional Awareness Scale (eLEAS) scoring system was applied to Adult Attachment Interviews (AAI) administered at baseline (n = 87; Mage = 27.4, SDage = 7.4) and upon treatment termination (n = 52; Mage = 28.6, SDage = 7.2). In both treatment groups, EWR at baseline was positively correlated with a reduction of NSSI after one year of psychotherapy (r = .46, p < .001). No significant correlations were found between baseline EWR and changes in other outcome measures. Compared to baseline, mean EWR scores significantly decreased after one year of treatment. Our findings indicate that a borderline patient’s ability to verbalize emotions might be a resource facilitating a reduction of NSSI in psychotherapy. We discuss strengths and limitations of applying the eLEAS scoring system to open-ended texts in a psychotherapy context. Given the exploratory nature of this study, replication in future studies is warranted. ClinicalTrials.gov (identifier NCT00714311, registration date 07/09/2008).
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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 | 0.002 |
| 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".