Expanding the impact of the Driving and Dementia Roadmap through a national radio ad campaign
Bibliographic record
Abstract
BACKGROUND: People with dementia and family and friend carers report feeling ill-equipped to manage the driving cessation process in dementia. Our online platform, the Driving and Dementia Roadmap, provides knowledge, resources and tools to help manage the driving cessation process and provide support for when driving stops. The objective of the present study was to explore the impact of a national radio ad campaign to expand the use of the Driving and Dementia Roadmap. METHODS: In January 2024, radio ads targeting people with dementia and family/friend carers were broadcasted to 5 Canadian provinces. Excluding regions with less than 4 users in January 2024, a total of 43 regions received the radio ad (coverage regions) and 39 regions did not (non-coverage regions). Google Analytics of the Driving and Dementia Roadmap were collected in January 2024 (broadcast period) and September-December 2023 (pre-broadcast period). Four pre-post outcomes were compared within- and between- regions: total users, new users, return users, and engaged sessions (i.e., visits > 10 seconds). Data were analyzed using Wilcoxon-Signed Rank tests for within-region comparisons and Mann-Whitney U tests for between-region comparisons. RESULTS: Within both coverage and non-coverage regions, the number of total users, new users, return users, and engaged sessions significantly increased during the broadcast period compared to pre-broadcast (all p's < .001). The pre-post broadcast increase in total users, new users, and engaged sessions was significantly greater within the coverage regions compared to non-coverage regions (all p's < .05), with the greatest increase being in the number of total users (p < .001). On average, the pre-post monthly broadcast increase in total users was four-fold larger within the coverage regions (M=20.19; SD=57.44) compared to non-coverage regions (M=5.22; SD=4.47). CONCLUSION: This national radio ad campaign had a significant impact on use of the Driving and Dementia Roadmap, increasing total users, new users, and engaged sessions. Overall, this expanded use will help more people with dementia and their family/friend carers access knowledge, resources, and tools on ways to better manage the complex process of driving cessation.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".