Promoting Evidence-Based Health Policy, Programming, and Practice for Seniors: Lessons from a National Knowledge Transfer Project
Bibliographic record
Abstract
ABSTRACT In response to Canada's pressing need for effective evidence-based policy, services, and practices specific to seniors, national leaders representing all concerned stakeholders designed and implemented a National Consensus Process to promote spread, exchange, choice, and uptake of research evidence on social and health issues associated with an aging population. This article presents the innovative methods and evaluation of this three-year project, illuminating for all constituencies the challenges and opportunities associated with promoting seniors' independence through collaborative knowledge transfer efforts. A total of 198 organizations and 65 individuals were surveyed at baseline, throughout the intervention, immediately post-intervention, and one year post-intervention. Knowledge from 783 studies was spread to 63,387 people, 90 per cent of whom reported knowledge exchange. Over 50 per cent of stakeholders reported using the research evidence, although processes for facilitating knowledge choice did not achieve consensus. Significant knowledge uptake occurred in two of the four research theme areas.
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 | 0.129 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".