Developing the Driving and Dementia Roadmap: a knowledge-to-action process
Bibliographic record
Abstract
OBJECTIVES: Despite three decades of research, gaps remain in meeting the needs of people with dementia and their family/friend carers as they navigate the often-tumultuous process of driving cessation. This paper describes the process of using a knowledge-to-action (KTA) approach to develop an educational web-based resource (i.e. toolkit), called the Driving and Dementia Roadmap (DDR), aimed at addressing some of these gaps. DESIGN: Aligned with the KTA framework, knowledge creation and action cycle activities informed the development of the DDR. These activities included systematic reviews; meta-synthesis of qualitative studies; interviews and focus groups with key stakeholders; development of a Driving and Dementia Intervention Framework (DD-IF); and a review and curation of publicly available resources and tools. An Advisory Group comprised of people with dementia and family carers provided ongoing feedback on the DDR's content and design. RESULTS: The DDR is a multi-component online toolkit that contains separate portals for current and former drivers with dementia and their family/friend carers. Based on the DD-IF, various topics of driving cessation are presented to accommodate users' diverse stages and needs in their experiences of decision-making and transitioning to non-driving. CONCLUSION: Guided by the KTA framework that involved a systematic and iterative process of knowledge creation and translation, the resulting person-centered, individualized and flexible DDR can bring much-needed support to help people with dementia and their families maintain their mobility, community access, and social and emotional wellbeing during and post-driving cessation.
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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 | 0.169 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".