Negotiating exceptions to clients’ problem discourse in consultative dialogue
Bibliographic record
Abstract
OBJECTIVE: The purpose of this qualitative study was to examine how consultants negotiated exceptions to clients problem or aspiration discourse in lifestyle consultations held for research purposes. DESIGN: Participants from a university campus (students and employees) were recruited for 1-hr lifestyle consultations with therapist consultants having graduate training and supervision in narrative and solution-focused therapy. The consultations were held with the expectation that consultants would, at least once, invite discussions of exceptions in client's problem or aspiration discourse. We wanted to understand how such discussions were initiated and brought to conclusion by examining client and consultants use of conversational practices. METHOD: Twelve volunteer 'clients' participated in consultations with our six volunteering consultants. These consultations were videotaped then passages were selected where consultants initiated exception discussions with the clients involved. The 18 selected passages were discursively analyzed for general rhetorical features evident in those passages, and three passages were transcribed and analyzed using conversation analysis to make evident more specific rhetorical features of exception discussions, as they were engaged in by consultants and clients. RESULTS: Ten general features of exception discussions were highlighted and the more specific conversational analyses revealed a 'messiness' that was related to how exception discussions were introduced and negotiated as a novel discourse in the consultations. CONCLUSIONS: We discuss our findings in the context of therapists' use of exception questions and discussions in therapy and highlight particular conversational practices and sensitivities relevant to engaging clients in such exception discussions.
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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 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| 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".