IKT Guiding Principles: demonstration of diffusion and dissemination in partnership
Bibliographic record
Abstract
INTRODUCTION: Integrated knowledge translation (IKT) is a partnered approach to research that aims to ensure research findings are applied in practice and policy. IKT can be used during diffusion and dissemination of research findings. However, there is a lack of understanding how an IKT approach can support the diffusion and dissemination of research findings. In this study, we documented and described the processes and outcomes of an IKT approach to diffusing and disseminating the findings of consensus recommendations for conducting spinal cord injury research. METHODS: Communication of the IKT Guiding Principles in two phases: a diffusion phase during the first 102 days from the manuscript's publication, followed by a 1147 day active dissemination phase. A record of all inputs was kept and all activities were tracked by monitoring partnership communication, a partnership tracking survey, a project curriculum vitae, and team emails. Awareness outcomes were tracked through Google Analytics and a citation-forward search. Awareness includes the website accesses, the number of downloads, and the number of citations in the 29 month period following publication. RESULTS: In the diffusion period, the recommendations were viewed 60 times from 4 different countries, and 4 new downloads. In the dissemination period, the recommendations were viewed 1109 times from 39 different countries, 386 new downloads, and 54 citations. Overall, during dissemination there was a 17.5% increase in new visitors to the website a month and a 95.5% increase in downloads compared to diffusion. CONCLUSION: This project provides an overview of an IKT approach to diffusion and dissemination. Overall, IKT may be helpful for increasing awareness of research findings faster; however, more research is needed to understand best practices and the the impact of an IKT approach on the diffusion and dissemination versus a non-partnered approach.
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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 | 0.022 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".