Change in Biased Thinking in a Treatment Based on the Motive-Oriented Therapeutic Relationship for Borderline Personality Disorder
Bibliographic record
Abstract
Biased thinking is a common feature of patients presenting with borderline personality disorder (BPD). For the treatment of BPD, it was shown that the individualizing of the treatment, by using the motive-oriented therapeutic relationship (MOTR), had a beneficial short-term effect on process and outcome. So far, it remains unclear what the role of early change in biased thinking is in these treatments. The present study aims to assess whether there is a link between the MOTR, change in biased thinking, and outcome. The sample (N = 60) is based on a randomized controlled trial with two conditions: (a) 30 patients in a 10-session version of psychiatric management, and (b) 30 patients in a 10-session version of psychiatric management augmented with the MOTR. For each patient, three sessions (intake, middle, late) were selected, transcribed, and rated using the Cognitive Errors Rating Scale (CERS). An overall decrease of negative cognitive errors during 10 sessions of treatment was observed, independently of the treatment condition. No specific effect related to change in biased thinking may be attributed to the individualizing of the treatment. These results are discussed with regard to mechanisms of change in treatments for BPD, in particular with regard to the central role that biased thinking, as well as the MOTR, might play early in treatment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".