A Group’s Level of Defensive Functioning Affects Individual Outcomes in Group Psychodynamic-Interpersonal Psychotherapy
Bibliographic record
Abstract
We used an actor–partner interdependence model (APIM) to study the association between the individual group member and other group therapy members’ defensive functioning on an individual group member’s treatment outcome. We hypothesized that (a) more adaptive individual defensive functioning at pretreatment will be significantly related to better treatment outcomes (i.e., lower binge eating and interpersonal distress) at 6 months post-treatment; and (b) more adaptive other group members’ defensive functioning at pretreatment will be significantly related to better treatment outcomes at 6 months post-treatment. Participants (N = 136) were individuals with BED enrolled in group psychodynamic-interpersonal psychotherapy (GPIP). Participants completed attachment interviews and were assessed on interpersonal distress and days binged at pre-treatment and 6 months post-treatment. The interview audio recordings were transcribed and used to code defensive functioning. We found that individual overall defensive functioning (ODF) scores at pretreatment were not significantly associated with binge-eating frequency or interpersonal distress at 6 months post treatment. Other group members’ mean ODF scores at pretreatment were significantly associated with individual interpersonal distress at 6 months post-treatment. However, the other group members’ mean ODF scores were not significantly associated with individual binge-eating outcomes at 6 months post treatment. Defensive functioning of other members of a therapy group may be particularly important for improving interpersonal functioning in individuals with BED.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".