Bibliographic record
Abstract
AIM: The aim of this article is to clarify the concept of knowledge translation (KT) to close the gap that exists between research knowledge and actionable nursing practice. BACKGROUND: KT addresses the research to practice gap that exists in healthcare. KT is often confused with other terms and needs to be defined further as a concept for clarification and application in nursing practice. DESIGN: Concept analysis using the Walker and Avant method. DATA SOURCES: Databases searched were OVID, CINAHL, ProQuest, Mendeley, Western Libraries, and Google Scholar. Keywords used were "knowledge translation", "knowledge", "translation", "evidence-based practice", "research dissemination". Abstracts were reviewed for relevance, and 27 articles available in full-text and in English from 2000 to 2018 were retained. Online dictionaries included Merriam-Webster. The ancestry method was also used to retrieve relevant articles. RESULTS: KT is one of many terms used to describe the concept of moving research to actionable practice in healthcare. Six attributes of KT were identified: collaboration, action, receptivity, process, translation, and improved healthcare outcomes. CONCLUSIONS: Nurses are responsible to provide the best care to their patients, and effectively using KT in nursing practice can ensure better outcomes for patients.
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 | 0.049 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".