A New Perspective on Emerging Knowledge Translation Practices; Comment on "Sustaining Knowledge Translation Practices: A Critical Interpretive Synthesis"
Bibliographic record
Abstract
The critical interpretive synthesis by Borst and colleagues offered a new perspective on knowledge translation (KT) sustainability from the perspective of Science and Technology Studies. From our applied health services perspective, we found several interesting ideas to bring forward. First, the idea that KT sustainability includes the ongoing activation of networks led to several future research questions. Second, while not entirely a new concept, understanding how KT actors work strategically and continuously with institutional rules and regulations to sustain KT practice was noteworthy. We add to the discussion by emphasizing the importance of non-researcher voices (clinicians, administrators, policy-makers, patients, carers, public) in sustaining KT practice. We also remind readers that the health ecosystem is dynamic and interdependent, where one system level influences and is influenced by another, and that these constant adaptations suggest that understanding KT practices cannot be a one-off event but represent repeated moments for transformative learning.
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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 | 0.060 | 0.178 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.036 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.071 | 0.088 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".