Conversational Errors and Common Ground Activities in Psychotherapy—Insights from Conversation Analysis
Bibliographic record
Abstract
<p>Many patients leave psychotherapy although in need. What can professional practitioners and researchers assume what happened? Trying to receive a response from these patients we too often are left without an answer. In this paper I introduce to psychotherapy discourse some concepts taken from linguistics and Conversation Analysis (CA). The reason is that what psychotherapists of every kind do is “talk-in-interaction”. During such talk Typical Problematic Situations (TPS) appear which are well known in a macro-analytic perspective (if a patient comes late to the session, does not talk or blackmails the therapist with suicide threat). However, there are many TPS that can be detected by a micro-analytic perspective only. CA is a tool helping to idenfity this type of TPS. One relevant CA-concept is Common Ground, a psychological and linguistic concept which requires special activity from both participants in an interaction. Conversational “errors” that risk to tear the Common Ground often go unnoticed. Presenting segments of transcribed therapy sessions I want to direct attention to the details of how ‘errors’ in Common Ground activity happen, how they are noticed and dealt with by skillfull therapists or how they can become repaired. Among others I use transcription details of two suicidal patients. The transcripts are from the CEMPP-Project (Conversation analysis of Empathy in psychotherapy process), conducted at IPU, Berlin. Thanks to a grant by Köhler-Stiftung, Germany.</p>
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| 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".