The role and theoretical evolution of knowledge translation and exchange in public health
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
BACKGROUND: There is an increased emphasis in public health research on effective models and strategies to support knowledge translation (KT), the exchange, synthesis and ethically sound application of research findings within a complex set of interactions among researchers and knowledge users. In other words, KT can be seen as an acceleration of the knowledge cycle-an acceleration of the natural transformation of knowledge into use (Canadian Institutes of Health Services Research. Knowledge Translation Strategy, 2004). The most recent conceptualizations consider the complexities of public health decision-making. The role of practitioners and communities is increasingly considered. METHODS: We identify, describe and discuss the theoretical underpinnings of KT and recommend a way forward to build the evidence for more effective practice. RESULTS: Theoretical perspectives increasingly influence research on KT in public health. A range of innovative work is being conducted to explore methods for KT using practical tools, often with the support of government. CONCLUSIONS: KT describes a crucial and to date under-developed element of the research process. There is an important gap in theoretically informed empirical studies of effectiveness of proposed approaches in public health, health promotion and preventive medicine, and thus much of the debate remains abstract. There is clearly an urgent policy need to establish the effectiveness of KT models in a range of contexts. This must include both the consideration of development and the utilization of knowledge.
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 | 0.052 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.002 |
| 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