MAPx (Mobility Aid Personalization): examining why older adults “pimp their ride” and the impact of doing so
Bibliographic record
Abstract
We all do this. We personalize things. We buy leopard-printed seat covers and fuzzy dice for our cars, and display action figures and photographs in our offices. Studying older adults who have extended this process of personalization to their mobility devices, the purpose of the mobility aid personalization (MAPx) project is to examine MAPx and its impact on the health and mobility of older adults. Using a qualitative research design, field observations and interviews were conducted with 72 older adults to gain an in-depth understanding of device customization from an emic (insider's) perspective. Findings illustrate that older adults personalize their devices for reasons of fun, function and fashion. MAPx - the process of purposefully selecting or modifying a mobility device to suit individual needs and preferences - was also found to promote health and mobility by encouraging device acceptance, increasing social participation, enhancing joy and preserving identity. MAPx makes an important contribution to our understanding of the complex relationship between older adults and assistive devices and provides a new approach to some old problems including falls, inactivity and social isolation. Encouraging MAPx is a promising rehabilitation strategy for promoting health and community mobility among the older adult population. Implications for Rehabilitation Personalizing an assistive device facilitates device acceptance, promotes health and well-beingand should be supported and encouraged in rehabilitative care. Choice, variety and access are critical aspects of assistive devices; vendors, manufacturers andpractitioners should work together to provide clients with a greater range of affordable optionsfor new devices. Function is more than mechanical or physical; social factors including social identity, stigma andsocial roles must be adequately considered and explicit in rehabilitative practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.024 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".