Benefits and Quality of Life Outcomes From Transportation Voucher Use by Adults With Disabilities
Bibliographic record
Abstract
Abstract People with disabilities often feel isolated from their communities, which in turn affects their emotional well‐being and their overall quality of life. Access to transportation is a substantial barrier to community participation and an improved quality of life for individuals with disabilities. Transportation voucher programs represent a cost‐effective way to provide access to transportation for people with disabilities, but few studies have empirically examined the effectiveness of such programs. The present study examined the perceived benefits of participating in a transportation voucher program in Michigan, a midwestern state in the United States with limited public transportation. Cross‐sectional survey data collected from a convenience sample of 73 participants of the transportation voucher program, funded by the state's Developmental Disabilities Council from 2005 to 2008, were analyzed. Participation outcomes were differentiated by age, sex, employment status, and type of disability using multivariate logistic regression analysis. More than two‐thirds (70%) of participants reported that their emotional well‐being and community participation had improved, and 54% of participants indicated that participation in the voucher program had resulted in better overall quality of life. The type of disability was an important factor in determining the degree of gain experienced, particularly in terms of community participation and overall quality of life. Findings suggest that resources spent on developing voucher programs have the potential to enrich the lives of persons with disabilities and their families. However, future longitudinal research along with a comparison group may be necessary to validate these preliminary findings on the benefits of vouchers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".