Generating knowledge while counseling families : case study research on therapeutic conversations in the Family Systems Care Unit in Switzerland
Bibliographic record
Abstract
Introduction: The Family Systems Care Unit is a university-run real-life lab offering family-systems-centered therapeutic conversations to burdened families based on the Calgary Family Assessment and Intervention Model and the Illness Beliefs Model. In previous studies, therapeutic conversations have alleviated stress and anxiety and facilitated problem-solving and family decision-making. Aim & Objectives: The overall aim of this research program is to understand how and why therapeutic conversations are supportive of families. Our objectives are to investigate how therapeutic conversations support families' functioning, health, and well-being and to explore how this support can be explained. Methods: Experienced, interprofessional family-systems care experts provide therapeutic conversations that are free of charge. We ask for consent to video-record and observe the therapeutic conversations through a one-way window for research. The conversations are transcribed verbatim and analyzed by applying qualitative case-study designs suitable for understanding the families and interventions within their complexity. Findings: We will present the first results of exemplary single-case studies revealing burdened families' complex demands and various needs. We will also demonstrate how therapeutic conversations support and strengthen these families in their everyday coping and symptom management. Discussion / Conclusion: Case-study is a convenient research design to gain in-depth insights into the process and effectiveness of therapeutic conversations while revealing the complexity of various interacting demands and strengths of families. Implication for Practice: The real-life lab setting simultaneously enables the collection of rich data for research and valuable support for families. This relation makes the real-life lab a setting for ethically sensitive and justifiable research
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".