Planning for Future Successes Among Rural Volunteer Driver Programs: Understanding Local Preferences of Prospective Users and Drivers
Bibliographic record
Abstract
Many rural citizens depend solely on their automobiles to meet their needs, but the health effects of aging can make driving impossible over time. Volunteer driver programs can be a solution where no alternatives exist, yet available tool-kits offer limited guidance for community data collection, analysis and interpretation of results, making success difficult to predict. The Transportation Research Board (TRB) AP060 Paratransit Committee at the TRB Annual Meeting in 2007 proposed a national research effort in this area to study “the factors…contribut[ing] to the success of volunteer driver programs in different settings”. This paper presents stated preferences from prospective users and volunteers regarding anticipated factors critical to the success of a rural volunteer driver program focused on medical appointments. An extensive engagement campaign among a rural area of 21,000 in New Brunswick, Canada returned 68 positive responses to involvement with the program (28 as users only, 7 as drivers only, 17 as volunteers only, and 16 with multiple roles). Most respondents were female and aged 25-65. The majority (82% and 79%) of prospective users felt it was important or very important to access local and regional medical appointments, respectively, while 40% felt the same for shopping trips. The majority (59%) of prospective drivers felt it was important or very important to be paid for mileage, while 23% felt the same about receiving a charitable receipt for their donation. Next steps include further research to predict ridership and volunteer supply, trip preferences, and policy development to address operational concerns, such as insurance.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".