Introduction to Knowledge Translation in Health Care Research
Bibliographic record
Abstract
Knowledge translation may be understood as the process of synthesizing, disseminating, and exchanging information to appropriate audiences. This process is an essential component of research, ensuring that research findings reach the knowledge users in a cost-efficient and meaningful manner. However, novice investigators may sometimes believe knowledge translation equates to presentations at research conferences and publishing in a journal. The objective of this paper is to address this misconception by providing an overview of the knowledge translation process for novice investigators. The authors will first define knowledge translation and describe its importance in health care research. Following this brief introduction, a framework for knowledge translation, the Knowledge-to-Action Cycle, will be examined to equip novice investigators with the essential knowledge and strategies they need to develop and implement successful research proposals. The information presented in this paper may enable novice investigators to understand and apply knowledge translation in their research, enhancing its applicability and usefulness. The authors conclude this paper by clarifying the various approaches, levels, and means of knowledge translation, and by constructing a brief overview of planning considerations for novice investigators engaging in knowledge translation.
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 | 0.055 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".