EVALUATION OF KNOWLEDGE MOBILIZATION IN GERONTOLOGY: DIGITAL TOOLS, PAPER TOOLS, OR BOTH?
Bibliographic record
Abstract
The purpose of this research was to evaluate the impact of pocket tools in digital and paper formats that contained evidence-based information about the core challenges of aging. The tools, based on current research in aging, were created by interdisciplinary teams for an established national/international knowledge mobilization network of older adults, students, policy makers, academics and practitioners. The overarching goal of the network was to place the most recent knowledge on aging in the hands of users in a rapid and straightforward way. Over 230 tools including care giving, financial literacy, legal issues, policing, dementia, mental health, ethnicity, elder mistreatment and technology were developed and have been utilized by over a million users nationally and internationally. All researchers, and community partners to this project have had strong vested interests in knowing if the tools were effective, which format worked best for their stakeholders, and how the information was used. The investigation included a survey of current users of the tools (n=800) to evaluate how the tools were used; digital and paper tool users (seniors, practitioners, caregivers) were compared on outcomes of effectiveness in 9 random clinical trials (n=783) and 40 respondents were interviewed in-depth about their challenges in using the tools. Here we report on the survey and the first random trial of tools, the outcomes, challenges and the roles and implications for international partners. The results suggested that both digital and paper tools were very helpful in uptake but the digital divide still existed according to age and education.
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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 | 0.210 | 0.395 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".