The reciprocal relationship between alliance and early treatment symptoms: A two-stage individual participant data meta-analysis.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Even though the early alliance has been shown to robustly predict posttreatment outcomes, the question whether alliance leads to symptom reduction or symptom reduction leads to a better alliance remains unresolved. To better understand the relation between alliance and symptoms early in therapy, we meta-analyzed the lagged session-by-session within-patient effects of alliance and symptoms from Sessions 1 to 7. METHOD: We applied a 2-stage individual participant data meta-analytic approach. Based on the data sets of 17 primary studies from 9 countries that comprised 5,350 participants, we first calculated standardized session-by-session within-patient coefficients. Second, we meta-analyzed these coefficients by using random-effects models to calculate omnibus effects across the studies. RESULTS: In line with previous meta-analyses, we found that early alliance predicted posttreatment outcome. We identified significant reciprocal within-patient effects between alliance and symptoms within the first 7 sessions. Cross-level interactions indicated that higher alliances and lower symptoms positively impacted the relation between alliance and symptoms in the subsequent session. CONCLUSION: The findings provide empirical evidence that in the early phase of therapy, symptoms and alliance were reciprocally related to one other, often resulting in a positive upward spiral of higher alliance/lower symptoms that predicted higher alliances/lower symptoms in the subsequent sessions. Two-stage individual participant data meta-analyses have the potential to move the field forward by generating and interlinking well-replicable process-based knowledge. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it