How Plan Analysis can inform the construction of a therapeutic relationship
Bibliographic record
Abstract
The construction of a positive therapeutic relationship was shown to be related with outcome in psychotherapy, but there are only a few prescriptive concepts helping the therapist to contribute to such a process. The present case illustrates the use of Plan Analysis (PA) and the motive-oriented therapeutic relationship (MOTR) in the explanation of the construction of a positive therapeutic relationship. We analyze the case of Sharon, a 22-year-old student presenting with major depressive disorder. We present the case formulation according to PA and select Session 7 from the therapeutic process to illustrate three moments of the therapist focus on the underlying motives: (a) a first moment when the therapist presents with nonoptimal features of responding to the patient's profile, (b) a second moment when the therapist intervenes optimally, and (c) a third moment when the therapist intervenes excellently. We discuss this case from the perspective of personalizing psychotherapy.
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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 | 0.015 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".