Development of a framework for knowledge translation: understanding user context
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.
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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 | 0.167 | 0.143 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.010 | 0.043 |
| Scholarly communication | 0.018 | 0.043 |
| Open science | 0.009 | 0.017 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".