Interpersonal impacts mediate the association between personality and treatment response in major depression.
Bibliographic record
Abstract
Personality, as characterized by the Five-Factor Model, predicts response to psychotherapy for depression. To explain how personality impacts treatment response, the present study investigated patient and therapist interpersonal processes in treatment sessions as an explanatory pathway. A clinical trial was conducted in which 103 outpatients (mean age: 41.17 years, 65% female) with primary major depressive disorder completed 16-20 weeks of cognitive-behavioral or interpersonal therapy. Before treatment, patients completed the Revised NEO Personality Inventory to assess personality domains (neuroticism, extraversion, openness-to-experience, agreeableness, and conscientiousness). After 3 and 13 weeks, patient interpersonal behavior was rated by the therapist and vice versa to determine levels of patient and therapist communal and agentic behaviors. Depression levels were measured before and after treatment. Structural equation modeling supported that patients' interpersonal behavior during therapy mediated the associations between pretreatment personality and depression treatment outcome. Specifically, extraversion, conscientiousness, and neuroticism (inverse) predicted higher levels of patient communion throughout treatment, which was in turn associated with improved treatment outcomes. Furthermore, patient agreeableness was inversely associated with agency throughout treatment, which was linked to poorer treatment response. Therapist interpersonal behavior was not a significant mediator. Results suggest that patient interpersonal behavior during treatment may be one way that patient personality impacts clinical outcomes in depression. Results underscore the clinical utility of Five-Factor Model domains in treatment process and outcome. (PsycINFO Database Record
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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 | 0.002 | 0.017 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".