Implementing Family Nursing: How Do We Translate Knowledge Into Clinical Practice?
Bibliographic record
Abstract
Health care systems worldwide are faced with the challenge of improving the quality of care, closing the knowledge-to-practice gap, and identifying the facilitators in these processes. Knowledge translation that promotes circularity between knowledge and practice is often overlooked. Knowledge transfer and translation are defined and briefly discussed in this article. Examples of knowledge translation in family nursing are provided, including knowledge creation research in pediatrics and adult pulmonary health at a University Hospital in Iceland. A second example focuses on the application of knowledge in mental health urgent care in a community health center in Calgary, Canada. Improving and speeding the circularity between knowledge translation and clinical practice reaps benefits for patients, families, health care providers, and the health care system. Conclusions about facilitating the implementation of family nursing knowledge into clinical practice are offered. The circularity between knowledge translation and practice is emphasized.
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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 | 0.105 | 0.262 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.011 | 0.007 |
| 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".