Exploring users' perspectives of the Driving and Dementia Roadmap
Bibliographic record
Abstract
Abstract Background Since October, 2022 the Driving and Dementia Roadmap (DDR) ( www.drivinganddementia.ca ) – an online resource to support people with dementia (PWD), family/friend carers (FCs) and healthcare providers (HCPs) as they navigate the challenges of driving cessation – has been accessed by over 34,000 users. To understand the DDR’s impact we are conducting on‐going surveys to explore users’ perspectives of the DDR. Method As users exit the DDR, they are invited, via pop‐up message, to participate in an online survey about their experiences with the DDR, including their perceived new knowledge and confidence gained due to using the DDR and their satisfaction with it. Descriptive statistics were conducted via the REDCap platform. Result To date, 81 DDR users have participated in the survey (17 PWD, 33 FCs and 31 HCPs). Topics rated the highest as “new knowledge gained” included: for PWD, “recognizing unsafe driving” (17.6%) and “getting around without driving” (17.6%); for FCs, “recognizing unsafe driving” (59.4%) and “learning about licensing and reporting” (59.4%); and for HCPs, “providing support after driving cessation” (61.3%) and “having discussions and managing emotions” (45.2%). With regards to increases in confidence reported as “somewhat” to “much more confident”, the highest rated topics included: for PWD, “making the decision to stop driving” (47.1%) and “recognizing unsafe driving” (41.2%); for FCs, “making the decision to stop driving” (71.9%) and “initiating conversations about driving cessation” (68.8%); and for HCPs, “managing the emotional impact” (71.1%) and “having conversations about driving cessation” (67.7%). Satisfaction levels by participants across all DDR characteristics were rated as “satisfied” to “very satisfied” by at least 47% of PWD, 87% of FCs and 80% by HCPs, with “trustworthiness of the information” rated the highest by all three groups (PWD: 75.1%; FCs: 93.8%; HCPs: 87.1%). Conclusion Early results indicate that a majority of users were satisfied with the DDR and using the DDR led to gains in new knowledge and increased confidence in managing aspects of driving cessation for all 3 groups, but least so for PWD. The next step will involve in‐depth interviews with participants to better understand the user experience and the relatively lower ratings among PWD.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".