Abstracts from the 17th International Family Nursing Conference held in Perth Australia, June 17-20, 2025
Bibliographic record
Abstract
Background and Purpose: Expert practice in therapeutic family conversations has been established at the Family Systems Care Unit (FSCU), a real-world clinical lab at a university of applied sciences. The FSCU supports families facing health-related challenges by fostering family strengths through therapeutic conversations. The FSCU invites students and professionals to observe and to engage in these expert-led therapeutic conversations as well as in clinical reasoning about this experience. As the unit moves towards formal reporting, the question arises: How can expert practice at the FSCU best be described from a practical perspective? The goal is to develop a framework. Models: The Calgary Family Assessment and Intervention Model, the Illness Beliefs Model and the Trinity Model are the clinical models implemented at the FSCU. Method: The clinical team at FSCU adopted an action research approach, drawing on data from family conversations, conversation records and internal team discussions. Results: The emerging framework organizes various elements, including family data, descriptions of expert practice, and additional outputs such as educational materials and collaborative efforts with the research team. For the clinical team, it is essential that the framework extends beyond family conversations themselves: Initial findings suggest that expert practice at FSCU is best understood as a reflexive practice - one that enhances systemic thinking and conceptualization. The framework is further enriched by considerations the diversity in families, family situations and the impact the conversations at the FSCU had on families’ everyday life. Conclusions and Implications: The development of this framework has allowed the team to clearly define and agree on the key concepts guiding the reporting and description of expert practice from a practice perspective. This framework invites reporting and is encouraging further exploration of practice-based approaches and expert practice to promote family strength globally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.280 | 0.060 |
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".