Failure to respond to the patient’s coaching: a case study of premature termination in psychodynamic psychotherapy
Bibliographic record
Abstract
One factor that may help avoid treatment failure by premature termination is the therapist’s responsiveness to the patient’s coaching. Coaching refers to the patient’s efforts to inform and guide the therapist about the kinds of therapist behaviors, attitudes, and interventions that would advance the therapy. The case of Barb, from a naturalistic study of time-limited psychotherapy, was examined as a failed treatment due to premature termination. Using a case study approach, descriptive analysis of the therapy process, along with quantitative ratings of coaching and responsiveness, was employed to examine instances of patient coaching with corresponding therapist responsiveness. Analysis indicated that the patient attempted to coach the therapist throughout each session about her plan for treatment and the responses she sought from the therapist. The therapist’s responses were only partially aligned––and in some instances not at all aligned––with the patient’s coaching. The disparity between the patient’s coaching and the therapist’s responsiveness was widest in the two sessions immediately prior to termination. This case study indicates that patients may coach therapists in order to avoid treatment failure. Therapists who attend to patient coaching and modify their responsiveness may be better able to sustain treatment and help patients achieve therapeutic goals.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.009 |
| 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".