Making Sense of Epistemological Conflict in the Evaluation of Narrative Therapy and Evidence-Based Psychotherapy
Bibliographic record
Abstract
This paper outlines the epistemological and theoretical formation of narrative therapy and implications for its evaluation. Two authoritative paradigms of psychotherapy evaluation have emerged in psychology since the mid- 1990s. The Clinical Division of the American Psychological Association established the empirically supported treatment (EST) movement. A more inclusive but medically emulative model of evidence based practice in psychology (EBPP) then emerged. Some therapies such as narrative therapy do not share the theoretical commitments of these paradigms. Narrative therapy is an approach that values a non-expert based, collaborative, political and contextual stance to practice that is critical of normalising practices of medical objectification and reductionism. Post-positivist theoretical influences constitute narrative therapy as a practice that values the social production and multiplicity of meaning. This paper problematises a conflictual relationship (a differend) between the evaluation of narrative therapy and evidence based psychotherapy. Firstly, it briefly outlines the EST and EBPP paradigms and their epistemology. This paper then provides an overview of some of the key epistemological and theoretical underpinnings of narrative therapy and concludes with some cautionary notes on its evaluation.
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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 | 0.321 | 0.517 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.030 | 0.027 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".