Assessing women veterans’ satisfaction with mobility devices
Bibliographic record
Abstract
Introduction This study aimed to assess women Veterans’ satisfaction with their primary mobility device and the related services they received. Methods Women Veterans who received a mobility device in the past 5 years from the Veterans Health Administration (VHA) completed an online survey containing the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST) about their primary mobility device. Scores were analyzed using descriptive statistics while open-ended comments were analyzed using inductive thematic analysis. Results 571 out of 4078 (14%) invited women completed a sufficient portion (>75%) of the QUEST. They reported high levels of satisfaction with their devices and services received (>4 out of 5 indicating ‘quite’ to ‘very’ satisfied). Despite this finding around 80% of the women left comments related to discontent with their device. Main and sub-themes that were consistent across all devices included equipment issues (mechanical design, lack of features or customizability, poor quality components/material), physical and psychological impacts of the device, usability issues, and unmet service needs (lack of efficiency, lack of quality, issues with service providers and lack of access). Discussion Women-centered design and delivery of mobility devices should be prioritized. Opportunities exist for VHA to make improvements within several areas in the service and provision process.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".