Therapeutic alliance, subsequent change, and moderators of the alliance–outcome association in interpersonal psychotherapy for depression.
Bibliographic record
Abstract
The therapeutic alliance has historically emerged as a pantheoretical correlate of favorable psychotherapy outcomes. However, uncertainty remains about the direction of the alliance-outcome link, and whether it is affected by other contextual variables. The present study explored (a) if early alliance quality predicted subsequent symptom change while controlling for the effect of prior symptom change in interpersonal psychotherapy (IPT) for depression, and (b) whether baseline patient characteristics moderated the alliance-outcome relation (to help specify conditions under which alliance predicts change). Data derived from an open trial of 16 sessions of individual IPT delivered naturalistically to adult outpatients (N = 119) meeting criteria for major depression. Patients rated their sociodemographic, clinical, and interpersonal characteristics at baseline, their alliance with their therapist at Session 3, and their depressive symptoms at baseline, after every session, and at posttreatment. Data were analyzed using hierarchical linear modeling. Results indicated that alliance quality did not predict subsequent depression change, controlling for prior depression change. However, a significant education by alliance interaction emerged in predicting quadratic depression change (γ = .0007, p = .03); patients with higher levels of education who reported good early alliances with their therapists had the most positively accelerated change trajectory (i.e., faster depression reduction), whereas patients with higher levels of education who reported poorer early alliances had the most negatively accelerated change trajectory (i.e., slower depression reduction). The findings may help clarify a specific condition under which alliance quality influences subsequent improvement in an evidence-based treatment for depression. (PsycINFO Database Record
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".