Popay J: The role and theoretical evolution of knowledge translation and exchange in public health
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 knowl-edge 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
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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 | 0.059 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.008 | 0.086 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".