Dyadic, longitudinal associations among outcome expectation and alliance, and their indirect effects on patient outcome.
Bibliographic record
Abstract
Research indicates that patient outcome expectation (OE) correlates with improvement, and that this association may be mediated by better patient-therapist alliances. However, despite OE and alliance being dyadic and dynamic constructs, most research on these direct and indirect associations has assessed these variables from only one dyad member's perspective and at single time points. Addressing these gaps, we used a longitudinal actor-partner interdependence model to first examine OE-alliance associations. Namely, we assessed "actor" effects (relation between each member's OE at 1 session and his or her own next session alliance) and "partner" effects (relation between each member's partner's OE at 1 session and his or her own next session alliance). Second, we tested whether significant actor or partner effects of OE on alliance translated into better patient outcomes (indirect effects). Analyses were conducted at within- and between-dyad levels. Data derived from a generalized anxiety disorder trial in which 85 patients received 15 sessions of either cognitive-behavioral therapy (CBT) or CBT integrated with motivational interviewing. After every session, patients and therapists rated OE and alliance, and patients rated their worry. At the within-dyad level, there were OE-alliance actor effects for both patients and therapists. There was also a within-dyad partner effect; when patients had greater OE at one session their therapists reported better next-session alliances. Finally, all within-dyad effects in turn related to lower subsequent worry. Results reveal ways in which session-by-session fluctuations in both patient and therapist OE translate into better outcomes through their influence on alliance quality. (PsycINFO Database Record (c) 2019 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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".