Interpersonal problems as a predictor of treatment outcome in adult depression: An individual participant data meta-analysis
Bibliographic record
Abstract
OBJECTIVES: Interpersonal problems are a fundamental feature of depression, but study-level meta-analyses of their association with treatment outcome have been limited by heterogeneity in primary studies' analyses and reported results. We conducted a pre-registered individual participant data meta-analysis (IPD-MA) to examine this relationship for adult depression. This meta-analytic strategy can reduce variability by standardizing data analysis across primary studies. METHODS: We included studies examining the efficacy of five treatments for adult depression and assessing interpersonal problems at baseline. One-stage IPD-MA was conducted with three-level mixed models to determine whether baseline overall interpersonal distress, agency, and communion predicted depressive symptom level at post-treatment, 12-month, and 24-month follow-up. The moderating effect of treatment type was also investigated. RESULTS: [0.05, 0.26], r = 0.16), indicative of smaller effect sizes. The agency and communion dimensions were not significantly related to outcome. Treatment type did not significantly moderate interpersonal distress-outcome associations. DISCUSSION: Results show a small association between patient baseline overall interpersonal distress and subsequent depression treatment outcome in brief treatments for depression. Further studies might require to account for therapist effects. Registration number osf.io/u46t7.
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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 | 0.030 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.064 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".