Technology Enabled Knowledge Translation
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
Because of the rapid growth of health evidence and knowledge generated through research, and growing complexity of the health system, clinical care gaps increasingly widen where best practices based on latest evidence are not routinely integrated into everyday health service delivery. Therefore, there is a strong need to inculcate knowledge translation strategies into our health system so as to promote seamless incorporation of new knowledge into routine service delivery and education to promote positive change in individuals and the health system towards eliminating the clinical care gaps. E-health, the use of information and communication technologies (ICT) in health which encompasses telehealth, health informatics, and e-learning, can play a prominently supportive role. This chapter examines the opportunities and challenges of technology enabled knowledge translation (TEKT) using ICT to accelerate knowledge translation in today’s health system with two case studies for illustration. Future TEKT research and evaluation directions are also articulated.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it