Implementing Family Nursing: How Do We Translate Knowledge Into Clinical Practice? Part II: The Evolution of 20 Years of Teaching, Research, and Practice to a Center of Excellence in Family Nursing
Bibliographic record
Abstract
The author's reflections on knowledge transfer/translation highlight the importance of the circular process between science and practice knowledge, leading to the notion of "knowledge exchange." She addresses the dilemmas of translating knowledge into clinical practice by describing her academic contributions to knowledge exchange within Family Systems Nursing (FSN). Teaching and research strategies are offered that address the circularity between science and practice knowledge. The evolution of 20 years of teaching, research, and clinical experience has resulted in the recent creation of a Center of Excellence in Family Nursing at the University of Montreal. The three main objectives of the Center uniquely focus on knowledge exchange by providing (a) a training context for skill development for nurses specializing in FSN, (b) a research milieu for knowledge "creation" and knowledge "in action" studies to further advance the practice of FSN, and (c) a family healing setting to support families who experience difficulty coping with health issues.
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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 | 0.044 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".