How Can Research Organizations More Effectively Transfer Research Knowledge to Decision Makers?
Bibliographic record
Abstract
A pplied research organizations invest a great deal of time, and research funders invest a great deal of money generating and (one hopes) transferring research knowledge that could inform decisions about health and health care. Basing these knowledge‐transfer activities on our evolving understanding of the most effective approaches to knowledge transfer will help us achieve value for money in our individual and collective investments in health services and health policy research. Research organizations and research funders can probably be excused for not basing their activities on research evidence until now, however, because the variety of relevant questions, target audiences, and disciplinary perspectives and methodological approaches used in empirical studies has made the identification of take‐home messages from this field of research a very difficult task. We provide an organizing framework for a knowledge‐transfer strategy and an overview of our understanding of the current knowledge for each of the five elements of the framework. The framework provides an overall approach to knowledge transfer that can be evaluated as a whole over long periods of time, as well as specific elements that can be evaluated and fine‐tuned over shorter periods of time.
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 | 0.383 | 0.501 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.010 | 0.031 |
| Scholarly communication | 0.047 | 0.075 |
| Open science | 0.009 | 0.026 |
| Research integrity | 0.033 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".