Biased thinking assessed by external observers in borderline personality disorder
Bibliographic record
Abstract
OBJECTIVES: Biased thinking (to some extent overlapping with the concepts of cognitive distortions and cognitive errors) is a key concept in cognitive therapy of borderline personality disorder (BPD). Specific contents and cognitive processes related to BPD functioning are known. However, most studies are based on self-report measures which present a number of important limitations, in particular the difficulty in assessing non-conscious processes infused by affect. So far, no studies were conducted using valid observer-rated methodology addressing the question of biased thinking in BPD as it unfolds spontaneously in session. DESIGN: This is a controlled interview study comparing two matched groups, BPD patients and healthy controls. METHODS: A total of N= 25 clinical dynamic interviews with patients presenting with BPD were transcribed and rated using the Cognitive Errors Rating Scale (Drapeau, Perry, & Dunkley, 2008); their cognitive profiles were compared to those of N= 25 healthy controls who underwent the same procedure. RESULTS: Overall, results indicated that no between-group difference in the frequency of specific biases was found. However, heightened levels of negative cognitive biases, in particular over-generalizing and fortune-telling, were associated with BPD. Furthermore, negative over-generalizing was associated with the number of BPD symptoms. CONCLUSIONS: These results have high levels of ecological validity and are promising for the refinement of cognitive theory of BPD. Clinical implications for assessment and intervention are discussed.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".