Piloting an innovative knowledge translation and exchange (KTE) approach on educational resources for caregivers
Bibliographic record
Abstract
Abstract Background Knowledge translation and exchange (KTE) is about moving knowledge into practice, involving stakeholders in an ongoing iterative process. The Alzheimer Society of Canada (ASC) approaches KTE by emphasizing ongoing collaborations with our primary audiences: people with lived experience of dementia, healthcare providers, and researchers. Knowing that people have diverse learning styles and preferences for accessing information, ASC is diversifying the range of educational resources that are currently provided through a traditional medium (i.e., printed information sheets) to a multimedia range of KTE tools that can better meet the needs of our audiences. Beginning with a pilot project that explored the practical application of KTE to a key education resource, ASC has developed an operational process with the involvement of stakeholders to help our audiences access and benefit from the information they need in a manner that accommodates them. Method A resource for the KTE pilot project was chosen based on a data driven approach to assess need and impact (i.e., number of website views, downloads, printed resource orders and feedback from stakeholders). Using ASC’s KTE framework and the results of an environmental scan that identified KTE approaches used by other organizations, the team operationalized the KTE framework through a focus on four dimensions: 1) audiences, 2) information channels, 3) feasibility and 4) accessibility of the resource. Key stakeholders, including Alzheimer Society support staff and family caregivers, collaborated with ASC on the development of the tools through focus groups. Result An infographic and a small video series on practical communication tips for caregivers were created. These KTE tools will support caregivers in staying connected to the person living with dementia at all stages of the disease; as the information is broad and digestible, it can be used by other audiences, such as healthcare providers. Conclusion The KTE pilot project is a stepping‐stone to establish a more integrative KTE approach to ASC’s educational resources. The process established through this project will ensure that those who turn to ASC for information can find reliable, up‐to‐date and evidence‐based content through a variety of tools that are engaging, easy to understand and accessible.
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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 | 0.063 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".