Technology-enabled knowledge translation: Frameworks to promote research and practice
Bibliographic record
Abstract
Knowledge translation articulates how new scientific insights can be implemented efficiently into clinical practice to reap maximal health benefits. Modern information and communication technologies can be effective tools to help in the collection, processing, and targeted distribution of information from which clinicians, researchers, administrators, policy makers in health, and the public can benefit. Effective implementation of knowledge translation through the use of information and communication technologies, or technology-enabled knowledge translation (TEKT), would benefit both the individual health professional and the health system. Successful TEKT in health requires cultivation and acceptance in the following key domains: Perceiving types of knowledge and ways in which clinicians acquire and apply knowledge in practice. Understanding the conceptual and contextual frameworks of information and communication technologies applied to health systems, particularly the push, pull, and exchange communication models. Comprehending essential issues in implementation of information and communication technologies and strategies to take advantage of emerging opportunities and overcome existing barriers. Establishing a common and widely acceptable evaluation framework in order that researchers can compare various methodologies in their rightful contexts in TEKT research and adoption. Achieving harmony and common understanding in these areas will go a long way in fostering a fertile and innovative environment to encourage research and advance understanding in this exciting domain of TEKT.
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 | 0.222 | 0.144 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.012 | 0.101 |
| Scholarly communication | 0.044 | 0.047 |
| Open science | 0.009 | 0.033 |
| Research integrity | 0.023 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".