A CALL FOR KNOWLEDGE TRANSLATION IN NURSING RESEARCH
Bibliographic record
Abstract
Despite universal acknowledgement that healthcare practice and policy should be informed by the best available evidence, gaps in the timely creation and mobilization of knowledge are pervasive and contribute to poor health outcomes.These gaps emerge from the failure to develop evidence that responds to 'real world' issues, along with lengthy delays in uptake. 1 As a result of these 'knowdo' gaps, nursing researchers are moving away from traditional form of research knowledge creation and transfer toward more contextual and collaborative modes of research.Knowledge translation (KT) is increasingly recognized as a practical and effective way to improve the use of evidence in healthcare practice and policy.2 The Canadian Institutes of Health Research defines KT as "a dynamic and iterative process that includes synthesis, dissemination, exchange and ethically sound application of knowledge to improve health, provide more effective health services and products, and strengthen the health care system".3 Since 2013, in Brazil, the Rede para Políticas Informadas por Evidências (Evidence-Informed Policy Network -EVIPNet Brasil), a global WHO initiative, has supported evidence-informed decision making in healthcare policy.4 They have produced 14 evidence syntheses and eight deliberative dialogues for knowledge synthesis.With its emphasis on building mutual understanding and leveraging relationships to strengthen the use of evidence in practice and policy, this is an example of an integrated KT (IKT) strategy.Nursing practice is grounded in caring and relational theory. 1 These theories guide nurses to deeply attend to understanding and responding to the needs and goals of those with whom we work.As nurse researchers, these relational and caring theories extend to how we do research by guiding us toward approaches where researchers and research users co-produce knowledge about something defined as meaningful.Given the shared relational foundations between nursing and IKT, nurses are well positioned to contribute to advancing science and practice in this field.
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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 | 0.550 | 0.698 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.012 | 0.088 |
| Scholarly communication | 0.053 | 0.111 |
| Open science | 0.018 | 0.053 |
| Research integrity | 0.076 | 0.065 |
| Insufficient payload (model declined to judge) | 0.034 | 0.012 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".