Verbal elaboration of distinct affect categories and BPD symptoms
Bibliographic record
Abstract
OBJECTIVES: The present study explores the relationship between the mentalization of distinct affect categories and the severity of borderline personality disorder (BPD) symptoms. Mentalization is assessed by both the level of verbal elaboration (VE) achieved by discrete affects (explicit mentalization) and the proportion of these individual affects in verbal expression (implicit mentalization). DESIGN AND METHODS: Sixty-four outpatients completed a series of questionnaires and took part in an interview designed to produce eight relationship episodes that involved four basic emotions: sadness, joy, anger, and fear (two of each). Affect mentalization was assessed with the Grille de l'Élaboration Verbale de l'Affect (GEVA), an observer-rated measure of levels of elaboration of verbalized affect, and the measure of affect content (MAC), which identifies the content of the verbalized affect (e.g., anger). Diagnostic criteria were obtained with the BPD scale of the Structured Clinical Interview for DSM-IV (SCID-II) questionnaire. Alexithymia was assessed with the 20-item Toronto Alexithymia Scale (TAS-20). RESULTS: The severity of BPD symptoms was related to lower levels of VE of sadness. It was also associated with a higher frequency of hostility directed against others. The level of VE of sadness and the proportion of hostility showed incremental predictive value of borderline symptomatology over demographic information, the presence of a depressive disorder and alexithymia. CONCLUSIONS: These findings point to an association between the severity of BPD symptoms and a difficulty mentalizing specific affective domains largely recognized as being central to borderline pathology, namely sadness and hostility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".