Conversational perspective of therapeutic outcomes: The importance of preference in the development of discourse
Bibliographic record
Abstract
Abstract Major theme: Evaluation of therapeutic outcome. Logical development of the theme: We suggest a way for practitioners and researchers to assess if they are on track in conversing towards client preferred goals. We offer a critique of more conventional approaches to studying therapeutic progress, suggesting how a discursive (i.e. focused on interaction and language use) lens can address these limitations. Through this lens we examine therapeutic progress evident in ‘preference work’, where clients demonstrably indicate, imply, agree and disagree with where the therapeutic conversation is heading. Such ‘preference work’ offers a form of evidence of within‐session outcomes in a process of reaching larger client preferred outcomes. Authors’ point of view: We present the results of conversation analysis – a qualitative approach to the study of therapy – to illustrate our discursive perspective on therapy progress and change. Implications: we suggest a way for practitioners to assess if they are on track in conversing towards client preferred goals. We propose that our interactional perspective may significantly contribute to bridging practice and research in therapy.
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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 | 0.097 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".