Knowledge Translation in Family Nursing
Bibliographic record
Abstract
In an effort to examine translation of family nursing knowledge to practice, the Landspitali University Hospital Family Nursing Implementation Project (2007-2011), was thoughtfully initiated in Reykjavik, Iceland and systematically evaluated. The mission was to implement family nursing in every department of the hospital. This publication is the first formal research report from this landmark project. The purpose of this research was to evaluate the effectiveness of a short-term therapeutic conversation intervention with families who were receiving health care services at the Children's Hospital at Landspitali University Hospital in Iceland related to childhood and adolescent acute and chronic illnesses. The therapeutic conversation was guided by Family Systems Nursing and used the Calgary Family Assessment and Intervention Models (Wright & Leahey, 2005, 2009). Families (N = 76) were randomly assigned to either an experimental group (short-term therapeutic conversation: n = 41) or to a control group (traditional care: n = 35). Parents in the experimental group reported significantly higher family support after the intervention, compared to the parents in the control group. Differences were noted between families experiencing acute versus chronic illnesses. Recommendations are made for conducting and designing intervention research with families experiencing the hospitalization of a child or adolescent.
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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 | 0.097 | 0.166 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".