Complementarity patterns in cognitive therapy for major depressive disorder
Bibliographic record
Abstract
Abstract Background Interpersonal theories suggest that cohesion between two individuals can be described by the degree to which they communicate harmoniously or, the complementarity , of their interactions (Gurtman, 2001; Kiesler, 1996). Friendly complementarity within therapy dyads is linked to both alliance and treatment outcome (Kiesler & Watkins, 1989; von der Lippe, Monsen, Rønnestad & Eilertsen, 2008; Samstag et al., 2008; Tracey, Sherry & Albright, 1999). Aim The purpose of this study was to identify complementarity patterns that may help or hinder the effectiveness of cognitive therapy ( CT ) for depression. Method The Structural Analysis of Social Behavior (Benjamin, 1974) was used to code CT sessions for individuals with depression. Dyads ( N = 16) were divided into high‐ and low‐change groups. Lag Sequential Analysis (Sackett, 1979) was used to analyse first order sequences in client and therapist interpersonal behaviour. Findings As expected, complementarity was better among high‐change dyads for affiliative behaviour. Low‐change dyads demonstrated stronger patterns of complementarity along the autonomy axis. Conclusion This preliminary investigation illustrates different communication patterns among high‐ and low‐change dyads. Implications for clinical practice are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".