An Introduction to the Knowledge Translation Special Issue of the <i>Canadian Respiratory Journal</i>
Bibliographic record
Abstract
Health sciences research continues to progress at an astounding pace, with the discovery of novel molecules, therapies and technologies to yield ever newer health-related knowledge. Yet, despite the time and resources devoted to identify these myriad new ways to improve health, evaluations of actual care consistently demonstrate gaps between this medical knowledge (‘what we ought to be doing’) and its application in practice (‘what we are doing’) (1). The obvious implication is that our patients are not reaping the full benefits of advances in medical knowledge. Accordingly, organizations around the world are increasingly recognizing the danger of investment in knowledge creation without complementary investment in knowledge implementation. This paradigm shift has given rise to the science of knowledge translation (KT). What is KT? The Canadian Institutes of Health Research (CIHR) defines KT as “a dynamic and iterative process that includes synthesis, dissemination, exchange and ethically-sound application of knowledge to improve the health of Canadians, provide more effective health services and products and strengthen the health care system” (2). More simply, KT is the act of closing the knowledge-to-practice gap, or the so-called ‘know-do’ gap. It subsumes all activities that occur after knowledge is created, including analysis of the magnitude of care gaps and barriers and facilitators to application of knowledge, development of strategies to overcome barriers and to utilize facilitators, and objective measurements of the success of these strategies and their sustainability (3).
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.
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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.129 | 0.046 |
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".