Using case formulation for prediction of the therapeutic alliance in treatment for borderline personality disorder.
Bibliographic record
Abstract
= 29; Kramer et al., 2014). For each patient (in both groups as post hoc analysis based on videos), we performed a Plan analysis case formulation (Caspar, 2019): the idiographic information from the formulation was translated into quantitative scores (on a Likert-type scale) assessing patient's interactional agreeableness (vs. antagonism; Zufferey et al., 2019). We modeled the session-by-session predictions of the progression of the therapeutic alliance-rated by the patient and the therapist-over the course of treatment, as a function of interactional agreeableness, the individualization of treatment, as well as their interaction with the session number. Patients with high levels of agreeableness have a significant increase in their alliance assessment over time. Treatment based on the case formulation predicted session-by-session increase of the therapeutic alliance as rated by the therapists. This study was the first to explore intra- and interindividual dynamics of the therapeutic alliance in relationship with idiographic information extracted from case formulations. The results may help understand relationship struggles at the beginning of therapy for complex clinical problems, such as borderline personality disorder. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.023 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".