Attachment style as a moderating influence on the efficacy of cognitive-behavioral and interpersonal psychotherapy for depression: A failure to replicate.
Bibliographic record
Abstract
Research on aptitude-treatment interactions, or patient characteristics that are associated with better outcome in one treatment than another, can help assign patients to the treatments that will be most personally effective. Theory and one past study suggest that adult attachment style might influence whether depressed patients respond better to cognitive-behavioral therapy (CBT) or interpersonal psychotherapy (IPT). Spurred by inconsistency in past aptitude-treatment interaction research in general, as well as concerns about the reproducibility of psychological research, we sought to replicate and extend the previous study that showed that high attachment avoidance was associated with greater depression reduction in CBT than in IPT and to improve upon that study methodologically. Using longitudinal hierarchical linear modeling, the present study examined whether, among 69 adults randomly assigned to CBT or IPT, rate of change in severity of depression symptoms was predicted by treatment condition, attachment style, and their interaction. We also conducted regression analyses to determine whether posttreatment depression was predicted by the same variables. As expected, CBT and IPT were equivalent in efficacy; however, unlike in the previous trial, there were no moderation effects of attachment. Interestingly, in some analyses, anxious attachment was associated with more positive outcomes and avoidant attachment with more negative outcomes across both treatments. The findings highlight the need for researchers to attempt replications of past studies using methods that might elucidate the reasons for discrepancies in results, and they also suggest that alternative approaches to aptitude-treatment interaction research may be more fruitful.
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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 | 0.033 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".