Illuminating the Processes of Knowledge Transfer in Nursing
Bibliographic record
Abstract
RATIONALE: Over the past 10 years, there has been a propensity to translate research findings and evidence into clinical practice, and concepts such as knowledge transfer, research dissemination, research utilization, and evidence-based practice have been described in the nursing literature. AIM: This manuscript shows a selective review of the definitions and utilization of these concepts and offers a perspective on their interrelationships by indicating how knowledge transfer processes are the basis of all the concepts under review. FINDINGS: Definitions and utilization of knowledge transfer in the literature have been influenced by educational and social perspectives and indicate two important processes that are rooted in the mechanisms of research dissemination, research utilization, and evidence-based practice. These processes refer to a cognitive and an interpersonal dimension. Knowledge transfer underlies a process involving cognitive resources as well as an interpersonal process where the knowledge is transferred between individuals or groups of individuals. CONCLUSION AND IMPLICATIONS: This manuscript can contribute to our understanding of the theoretical foundations linking these concepts and these processes by comparing and contrasting them. It also shows the value and empirical importance of the cognitive and interpersonal processes of knowledge transfer by which research findings and evidence can be successfully translated and implemented into the nursing clinical practice.
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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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".