How collaboration in therapy becomes therapeutic: The therapeutic collaboration coding system
Bibliographic record
Abstract
BACKGROUND: The quality and strength of the therapeutic collaboration, the core of the alliance, is reliably associated with positive therapy outcomes. The urgent challenge for clinicians and researchers is constructing a conceptual framework to integrate the dialectical work that fosters collaboration, with a model of how clients make progress in therapy. AIM: We propose a conceptual account of how collaboration in therapy becomes therapeutic. In addition, we report on the construction of a coding system - the therapeutic collaboration coding system (TCCS) - designed to analyse and track on a moment-by-moment basis the interaction between therapist and client. Preliminary evidence is presented regarding the coding system's psychometric properties. The TCCS evaluates each speaking turn and assesses whether and how therapists are working within the client's therapeutic zone of proximal development, defined as the space between the client's actual therapeutic developmental level and their potential developmental level that can be reached in collaboration with the therapist. METHOD: We applied the TCCS to five cases: a good and a poor outcome case of narrative therapy, a good and a poor outcome case of cognitive-behavioural therapy, and a dropout case of narrative therapy. CONCLUSION: The TCCS offers markers that may help researchers better understand the therapeutic collaboration on a moment-to-moment basis and may help therapists better regulate the relationship.
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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 | 0.030 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".