Transition from driving to driving-cessation: experience of older persons and caregivers: a descriptive qualitative design
Bibliographic record
Abstract
BACKGROUND: For some older persons, driving is essential to maintain their daily activities and engagement with society. Unfortunately, some will have to stop driving, as they age. Driving-cessation is an important transition for older persons and caregivers, well known to cause significant challenges and consequences. This study aimed to describe the experience of older persons and caregivers in the transition from driving to ceasing to drive. METHODS: Within a descriptive qualitative design, semi-structured interviews were undertaken with older persons (n = 8) and caregivers (n = 6) from the city of Québec (Quebec, Canada), from November 2020 to March 2021. Using an inductive approach, the qualitative data was analyzed with the content analysis method. RESULTS: Some older persons had never thought they might someday lose their driver's license. The process of legislative assessment was unknown by almost all older persons and caregivers. The process was therefore very stressful for the research participants. Driving-cessation is a difficult transition that is associated with loss of independence, freedom, spontaneity, and autonomy. Qualitative analysis of data showed different factors that positively or negatively influence the experience of ceasing to drive, such as the older person's ownership of the decision, the presence of a network of friends and family, and self-criticism. There was significant impact related to driving-cessation for caregivers, such as assuming the entire burden of travel, psychologically supporting older persons in their grief, and navigating the driver's licensing system. CONCLUSIONS: These study results could help organizations and healthcare professionals to better accompany and support older drivers and caregivers in the transition from driving to driving-cessation. TRIAL REGISTRATION: None.
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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".