Let's Talk About It: A Narrative Review of Digital Approaches for Disseminating and Communicating Health Research and Innovation
Bibliographic record
Abstract
Best health practice and policy are derived from research, yet the adoption of research findings into health practice and policy continues to lag. Efforts to close this knowledge-to-action gap can be addressed through knowledge translation, which is composed of knowledge synthesis, dissemination, exchange, and application. Although all components warrant investigation, improvements in knowledge dissemination are particularly needed. Specifically, as society continues to evolve and technology becomes increasingly present in everyday life, knowing how to share research findings (with the appropriate audience, using tailored messaging, and through the right digital medium) is an important component towards improved health knowledge translation. As such, this article presents a review of digital presentation formats and communication channels that can be leveraged by health researchers, as well as practitioners and policy makers, for knowledge dissemination of health research. In addition, this article highlights a series of additional factors worth consideration, as well as areas for future direction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.082 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".