Realizing Relational Preferences Through Transforming Interpersonal Patterns
Bibliographic record
Abstract
Family therapy has often been conceptualized as a conversational process whereby therapists and clients generate new meanings. Based on a 3-year study of conversational practices observable in successful family therapy processes of Chilean families with a child/adolescent who is engaged in disruptive behaviors, we looked for clinical examples of Transforming Interpersonal Patterns (TIPs). TIPs are a key aspect of the IPscope, a framework we used to explore the meaning-making processes in family therapy. TIPs constitute a novel approach to explore therapeutic processes by identifying empirically traceable conversational practices involved in generating "new meanings." TIPs are involved in bringing forth and discursively articulating ("talking-into-being") clients' preferred ways of relating and living (i.e., relational preferences or RPs). We analyze conversational data from successful family therapy sessions/treatments, and present an emergent model of five categories of conversational practices making up TIPs, namely: Preparatory TIPs, Identifier TIPs, Tracker TIPs, Transformer TIPs, and Consolidator TIPs. We have called them "realizers" because these conversational practices help families talk-into-being (or "make real") particular relational preferences. We also offer user-friendly descriptors of realizers' subcategories (e.g., Measuring TIPs) which may help practitioners to recognize, learn, and perform these conversational invitations. Theoretical consequences and future lines of research are discussed.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".