APPLYING KNOWLEDGE TRANSFER STRATEGIES IN THE DEVELOPMENT OF RESOURCES FOR OPTIMAL AGING
Bibliographic record
Abstract
Effective knowledge transfer (KT) of research findings into practical applications may allow users to make informed health decisions (Boström et al., 2012). Increasingly, KT strategies are utilizing technology and the digitization of health-related resources. Currently, little is known about KT within aging populations, specifically those that are in digital format. Using the Conceptual Model of Knowledge Exchange (Meagher et al., 2008) as a guiding framework, a multi-phase KT initiative was implemented to promote and evaluate paper and digital resources developed by the National Initiative for the Care of the Elderly (NICE). Results from the initiative’s first phase indicate that ideas/recommendations from the tools were adopted by users (40%), the tools improved users’ knowledge and understanding on the topic (42%), and information presented in the tools is often favoured over other resources providing similar content (60%). Results on the KT format indicate that both professionals/clinicians and older adults prefer the paper-based format of the resources. These findings suggest that the challenge for effective KT is to ensure that the digitization of KT resources does not outpace their adoption by end-users and key stakeholders. For KT to be successful, the process must include the development of evidence-based resources, include the perspectives of stakeholders, and be easy to apply in various settings. Given that little is known about KT with aging populations, the described initiative provides stakeholders serving this population with practical approaches for assessing the impact of their own digital KT initiatives.
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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 | 0.074 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".