Relation of patient and therapist interpersonal impact messages to outcome in interpersonal therapy for depression.
Bibliographic record
Abstract
Interpersonal depression theories posit that excessive submissiveness in social interactions perpetuates negative mood. Correspondingly, many psychotherapies postulate that improvement can be facilitated by patient-therapist interactions. However, few studies have tested in-session patient and therapist behaviors that should, in theory, associate with depression reduction. Addressing this gap, the present study examined such associations in interpersonal psychotherapy (IPT). We hypothesized that decreases in patients´ submissive interpersonal impacts on their therapist would be associated with greater depression reduction, as would increases in therapists´ friendly submissive impacts on their patient; theoretically, such therapist behavior would pull for patients to complement it with adaptive assertiveness, thereby disrupting their submissive tendencies. Data derived from an open trial of 16 IPT sessions for adults with major depression. Patients (N = 119) and therapists (N = 39) rated the others´ interpersonal impacts at Sessions 3 and 16 via the Impact Message Inventory. Patients rated their depression on the Beck Depression Inventory-Second Edition after each session. As predicted, multilevel modeling revealed that decreases in patients´ submissive impacts were associated with greater concurrent depression reduction (p = .03) and lower posttreatment depression level (p = .03). Also, although therapists did not differ in their change in friendly submissive impacts, thus precluding a test of the influence of such change on outcome, a greater average level of therapist friendly submissiveness related to lower posttreatment depression (p = .008). Results support interpersonal depression theories and the therapeutic benefit of specific patient and therapist change processes in IPT.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".