Can we streamline the concepts of knowledge translation, dissemination and implementation for lay stakeholders? A perspective
Bibliographic record
Abstract
OBJECTIVE: To initiate a critical dialogue within the evidence-based practice (EBP) communities regarding the necessity of clear and accessible concepts that bridge the gap between research and practical use for non-expert stakeholders. KEY ARGUMENTS: There has been consistent evidence of failure to translate high-quality biomedical and health research findings into clinical practice and policy implementation. Research findings are not making their way into practice in a timely fashion and are believed to take two decades before an intervention can make its way to patients' bedsides. Numerous concepts, models and theories have been developed to address this research application gap to guide experts in effectively applying research outcomes to practice. Unfortunately, there are no simplified descriptions of these concepts for use by lay stakeholders, such as patient representatives who may contribute meaningfully to clinical and other health research. To address this gap, as a first step towards developing and validating user-friendly concepts, we propose definitions for three commonly used concepts: knowledge translation, dissemination and implementation in a lay language. We also offer a simplified framework that connects these concepts. The suggested definitions and framework need refinement and confirmation from a broad range of non-expert stakeholders. CONCLUSION: Insufficient simplified definitions to explain research in practical terms have led to confusion among stakeholders with limited expertise in EBP. In this context, scientific knowledge that is easy to comprehend and use is vital for non-experts to engage meaningfully and speed up the application of clinical research outcomes in patient care.
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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 | 0.415 | 0.336 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.014 | 0.173 |
| Scholarly communication | 0.045 | 0.075 |
| Open science | 0.010 | 0.033 |
| Research integrity | 0.046 | 0.049 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".