How is Systemic and Constructionist Therapy Change Process Narrated in Retrospective Accounts of Therapy? A Systematic Meta‐synthesis Review
Bibliographic record
Abstract
Despite the considerable potential of qualitative approaches for studying the systemic and constructionist therapy process due to shared theoretical and epistemological premises, to date there is lack of a comprehensive qualitative synthesis of how change process is experienced and conceptualized by clients and therapists. To address this evidence gap, we performed a systematic meta-synthesis review of 30 studies reporting clients' and therapists' retrospective narratives of change process across systemic and constructionist models and across a range of client configurations, including individuals, couples, families, and groups. The studies were identified following a systematic search in PsycINFO and MEDLINE resulting in 2,977 articles, which were screened against eligibility criteria. Thematic analysis led to the identification of four main themes: (1) navigating through differences, (2) toward nonpathologizing construction of problems, (3) navigating through power imbalances, and (4) toward new and trusting ways of relating. Findings illustrate the multifaceted aspects of systemic and constructionist change process, the importance for their reflexive appraisal, and the need for further research contributing to the understanding of the challenges inherent in the systemic and constructionist therapeutic context.
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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 | 0.054 | 0.173 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.022 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".