Using Complexity to Simplify Knowledge Translation Comment on "Using Complexity and Network Concepts to Inform Healthcare Knowledge Translation"
Bibliographic record
Abstract
Putting health theories, research and knowledge into practice is a challenge referred to as the knowledge-toaction gap. Knowledge translation (KT), and its related concepts of knowledge mobilization, implementation science and research impact, emerged to mitigate this gap. While the social interaction view of KT has gained currency, scholars have not easily made a link between KT and the concept of complexity. Kitson and colleagues suggest we ought to examine the role of complexity in KT processes using defined theories and concepts borrowed from network and complex adaptive systems theory. They further argue that better KT outcomes might be achieved using this new lens. There remain, however, several critical considerations for this sort of theory application to work in the real-world. Complexity and network theory offer explanatory power about the KT problem, but these theories are less helpful for understanding solutions.
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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 | 0.013 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.059 | 0.066 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".