Assessing the psychosocial impact of mobility assistive technology on women Veterans
Bibliographic record
Abstract
Introduction: Despite the growing number of women Veterans with disabilities, data on how well mobility assistive technology (MAT) meets their needs are limited. Evaluating psychosocial impact is key to ensuring that MAT not only meets physical needs but also fosters social participation, mental well-being, and independence. The aim of this study was to examine the perceived psychosocial impact of using MAT on women Veterans' competence, adaptability, and self-esteem. Methods: Women Veterans (N = 501) who received MAT from the U.S. Department of Veterans Affairs within the past five years completed a national online survey including the Psychosocial Impact of Assistive Devices Scale (PIADS). Women were asked to score the PIADS on the basis of their experiences using their primary device type: cane, leg-foot orthosis, walker, power wheelchair (PWC), scooters, manual wheelchair (MWC), or crutches. Results: Participants expressed an overall positive psychosocial impact of MAT on competence (mean = 1.03, SD = 1.23), adaptability (mean = 0.76, SD = 1.43), and self-esteem (mean = 0.67, SD = 1.22). Around 17% of the total item responses indicated a negative perceived psychosocial impact of using MAT. PWC users' item scores indicated higher positive psychosocial impact than those of other device-type users (0.001 < p < 0.023). Discussion: Despite the overall positive psychosocial benefits of MAT, use was associated with feelings of low self-esteem, frustration, and embarrassment. The results highlight a possible mental health benefit for PWC users compared with other MAT users.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".