A longitudinal approach to evaluating therapist and client contributions to alliance on outcomes in borderline personality disorder.
Bibliographic record
Abstract
Methods for studying therapeutic alliance have primarily examined the impact of the early alliance on outcomes. This does not allow for an understanding of the contributions of client, therapist, and dyadic factors to the alliance. Also, the alliance may change over time, highlighting the need for longitudinal methods. Efforts have been made to develop approaches that decompose the contributing factors and their impact on outcomes, but these findings may not apply to clients with borderline personality disorder (BPD). Our study extends previous research by replicating Baldwin et al.'s (2007) approach to disentangling therapist versus client-therapist dyad effects while using a time-varying framework. Participants (n = 156) were individuals diagnosed with BPD randomized to 1 year of dialectical behavior therapy or general psychiatric management. Outcomes were general psychiatric severity and interpersonal functioning measured at baseline and every 4 months. Client-rated alliance was measured at these time points. Early alliance predicted interpersonal functioning (p = .0132) with a significant contribution from clients (p = .0248) but not therapists (p = .2854). In the time-varying analysis, client contribution to the alliance was significant (p = .0022). For general psychiatric severity, client contributions to the alliance were significant (p < .0001) but not therapist contributions (p = .6779). Client contribution to the alliance was significant (p = .0168) in the time-varying model. Results suggest that in a BPD sample, time-varying alliance is a better predictor of rate of change in outcomes compared with the alliance measured at a single time point. In contrast to other studies, client, not therapist, contributions to alliance were significant in predicting outcomes. (PsycInfo Database Record (c) 2022 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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".