Some Theoretical Underpinnings of Knowledge Translation
Bibliographic record
Abstract
A careful analysis of the definition of knowledge translation highlights the importance of the judicious translation of research into practice and policy. There is, however, a considerable gap between research and practice. Closing the research‐to‐practice gap involves changing clinical practice, a complex and challenging endeavor. There is increasing recognition that efforts to change practice should be guided by conceptual models or frameworks to better understand the process of change. The authors conducted a focused literature search, developed inclusion criteria to identify planned action theories, and then extracted data from each theory to determine the origins, examine the meaning, judge the logical consistency, and define the degree of generalizability, parsimony, and testability. An analysis was conducted of the concepts found in each theory, and a set of action categories was developed that form the phases of planned action. Thirty‐one planned action theories were identified that formed the basis of the analyses. An Access database was created, as well as a KT Theories User's Guide that synthesizes all the planned change models and theories, identifies common elements of each, and provides information on their use. There are many planned change models and frameworks with many common elements and action categories. Whenever any planned change model is used, change agents should consider documenting their experiences with the model so as to advance understanding of how useful the model is and to provide information to others who are attempting a similar project.
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 | 0.063 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.008 | 0.065 |
| Scholarly communication | 0.021 | 0.029 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".