Implementing and evaluating the driving and dementia roadmap (DDR) during the COVID pandemic
Bibliographic record
Abstract
Abstract Background To address the gap in evidence‐based interventions and resources to support people living with dementia (PWD) and family/friend carers (FCs) through the challenging process of driving cessation, we developed a web‐based educational resource called the Driving and Dementia Roadmap (DDR). An implementation evaluation, which took place during the COVID pandemic, was conducted to explore the delivery, acceptability, adaptability, preliminary benefits and limitations of the DDR. Method The DDR was delivered to Alzheimer Society (AS) clients by staff from six organizations in four Canadian provinces from December 2020 to September 2021. Semi‐structured interviews were conducted with 19 AS staff, eight PWD and 13 FCs. In addition to questions about their experiences of delivering and using the DDR, participants were asked about the impact of COVID on using the DDR. Data were examined using a thematic analysis approach. Result AS staff reported that client concerns about driving cessation and the need for the DDR were less than anticipated due to COVID. They attributed this to other pressing issues such as the need to stay safe from COVID, a lack of access to services and activities, caregiver burnout and PWD’s isolation. FCs and PWD also indicated that driving was not an immediate concern because they were driving less in the pandemic. However, AS staff expressed apprehensions about increased driving risk in the aftermath of COVID due to reports of PWD’s profound cognitive decline and lack of driving experience during the pandemic. Conclusion Although COVID’s impact on driving cessation initially lessened PWD’s and FCs’ urgency in attending to this issue, the longer‐term implications of neglecting this issue may be considerable for PWD and FC. The need for resources, such as the DDR, to support PWD and FCs in the decision‐making and transition to non‐driving will be particularly critical post‐COVID.
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 | 0.068 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".