Development of a framework for knowledge translation: understanding user context
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To develop a framework that researchers and other knowledge disseminators who are embarking on knowledge translation can use to increase their familiarity with the intended user groups. METHODS: The framework was derived from a review and analysis of the knowledge translation literature and from the authors' own experience with a variety of user groups. RESULTS: The framework consists of five domains: the user group, the issue, the research, the knowledge translation relationship, and dissemination strategies. Within each domain, the framework includes a series of questions. The questions provide the researcher with a way of organizing what he or she already knows about the user group and the knowledge translation project, of identifying what still is unknown, and of flagging what is important to learn. CONCLUSIONS: Most researchers wishing to engage in knowledge translation are moving out of their own familiar contexts. By using this framework, researchers will learn about the new contexts in which they find themselves. The insights they gain will increase their familiarity with the user group, thus aiding in the implicit goal of the interactive model of knowledge translation: making the researcher a part of the user group context.
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.
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 | 0.038 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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 it