The case for knowledge translation: shortening the journey from evidence to effect
Bibliographic record
Abstract
A large gulf remains between what we know and what we practise. Eisenberg and Garzon point to widespread variation in the use of aspirin, calcium antagonists, blockers, and anti-ischaemic drugs in the United States, Europe, and Canada despite good evidence on their best use. 1 Such variation is common not only internationally but within countries. 2 Large gaps also exist between best evidence and practice in the implementation of guidelines. Failure to follow best evidence highlights issues of underuse, overuse, and misuse of drugs 3 and has led to widespread interest in the safety of patients. ot surprisingly, many attempts have been made to reduce the gap between evidence and practice. These have included educational strategies to alter practitioners' behaviour 5 and organisational and administrative interventions. We explore three constructs: continuing medical education (CME), continuing professional development (CPD), and (the newest of the three) knowledge translation (box). Knowledge translation both subsumes and broadens the concepts of CME and CPD and has the potential to improve understanding of, and overcome the barriers to, implementing evidence based practice.
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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 | 0.553 | 0.679 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.019 | 0.011 |
| Science and technology studies | 0.012 | 0.072 |
| Scholarly communication | 0.045 | 0.097 |
| Open science | 0.015 | 0.054 |
| Research integrity | 0.055 | 0.064 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".