Development of the Guide to Disseminating Research (GuiDiR): A consolidated framework
Bibliographic record
Abstract
BACKGROUND: Less than one third of research evidence is translated into policy or practice. Knowledge translation requires effective dissemination, adoption and finally implementation. These three stages are equally important, however, existing knowledge translation models and frameworks provide little and disparate information about the steps and activities required for effective dissemination. OBJECTIVE: This study aimed to empirically develop a consolidated framework of evidence-based steps and activities for disseminating research evidence. METHODS: We identified models and frameworks from a scoping review and dissemination and implementation webtool. We synthesised them into a prototype dissemination framework. Models and frameworks were eligible to inform steps in our framework if they fulfilled at least one of three elements of dissemination: intending to generate awareness of a message, incorporates targeting an audience: tailoring communication. An initial coding framework was created to organise data into dissemination steps. Drawing on 'co-approach' principles, authors of the included models and frameworks (dissemination experts) and health service researchers (end users) were invited to test and refine the prototype framework at a workshop. RESULTS: From 48 models and frameworks reviewed, only 32 fulfilled one or more of the three dissemination elements. The initial coding framework, upon refinement, yielded the Guide to Disseminating Research (GuiDiR) comprising five steps. 1) Identify target audiences and dissemination partners. 2) Engage with dissemination partners. 3) Identify barriers and enablers to dissemination. 4) Create dissemination messages. 5) Disseminate and evaluate. Multiple activities were identified for each step and no single model or framework represents all steps and activities in GuiDiR. CONCLUSIONS: GuiDiR unifies dissemination components from knowledge translation models and frameworks and harmonises language into a format accessible to non-experts. It outlines for researchers, funders and practitioners the expected structure of dissemination and details the activities for executing an evidence-based dissemination strategy.
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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 | 0.669 | 0.582 |
| Meta-epidemiology (narrow) | 0.006 | 0.009 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.046 | 0.040 |
| Science and technology studies | 0.012 | 0.038 |
| Scholarly communication | 0.044 | 0.044 |
| Open science | 0.025 | 0.040 |
| Research integrity | 0.027 | 0.041 |
| Insufficient payload (model declined to judge) | 0.007 | 0.010 |
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".