The case for knowledge translation: shortening the journey from evidence to effect
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
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.
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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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