Lifestyle Adjustment and Mobility-Related Goal Setting After Driving Cessation With People Living With Dementia
Bibliographic record
Abstract
Abstract Community mobility is an important social determinant of health. For people living with dementia, the forfeiture of a driving licence can signal a loss of independence, limiting access to activities outside of the home. Loss of community connectivity and social participation has a substantial impact on quality of life and may lead to depression and more rapid cognitive decline. This study is focused on a driving cessation intervention that helps people with dementia identify personal goals that are framed around community mobility and adjusting to life without driving. Health professionals work with participants to translate these into specific, practical and achievable outcomes by program end. Participants may nominate more than one goal. This study reports on goal setting and achievement. Using a modified version of the Canadian Occupational Performance Measure it examines pre- to post-intervention achievement of, and satisfaction with, identified goals for 17 participants living with dementia aged 63-93 (M=75.24, 76% male) from regional and metropolitan Australia. Thematic analysis of clinical interviews and field notes highlighted the range of desired goals, and the challenges posed and problem-solving strategies used in setting realistic, non-driving goals. Significant positive improvements were found across a total of 29 goals for (i) performance t(28) = -10.01, p < .000, and (ii) satisfaction, t(28) = -10.32, p < .000. The implications for practice are that supportive goal-setting of personally relevant objectives and valued activities following driving cessation may be effective in lessening some of the negative effects of giving up driving for people with dementia.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".