Narrative and Solution-Focused Therapies: A Twenty-Year Retrospective
Bibliographic record
Abstract
The Therapeutic Conversations (TC 1) Conference in Tulsa, Oklahoma in 1991 was a historic event in the advancement of postmodern therapies. We (David, a narrative therapist, and Jeff, a solution-focused therapist) were profoundly affected by this summit of the pioneering voices in narrative, solution-focused, strategic, and systemic therapy. This article highlights the evolution of both narrative and solution-focused therapy since TC 1 from our distinct, but overlapping vantage points. We have noted the increased differentiation of these approaches therapies since they were first compared (Chang & Phillips, 1993). While this differentiation is significant, we note that a hybrid of narrative and solution-focused therapy is being practiced among new practitioners, a development that may not have been predicted or hoped for by first and second-generation narrative and solution-focused therapists. This development is situated within the current climate of evidence-based practice, the recovery model of mental health, positive psychology, strength-based approaches, and the recent emphasis on resilience. Finally, we comment on the perils and possibilities of current developments and speculate as to what this might mean for the future of both approaches.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".