Understanding How a Public Transportation Network Training Program Can Improve the Self-Efficacy, Satisfaction and Experience of Community Mobility Among People with Disabilities: A Mixed Methods Research
Bibliographic record
Abstract
The Réseau de transport de la Capitale (RTC), Quebec City’s public transportation provider, has launched a training program to enhance skills and self-efficacy for using the bus, including training for people with disabilities: “Service d’accompagnement en mobilité intégrée (SAMI)”. This pre-post study with a convergent mixed approach aimed to evaluate the influence of the SAMI program (P-SAMI) on transportation self-efficacy, mobility and satisfaction with the bus use among people with disabilities. The study also explored people with disabilities’ experiences and perceptions with the P-SAMI and bus use. The P-SAMI was delivered, and questionnaires and semi-structured interviews were completed before and after P-SAMI. Paired t-tests, Wilcoxon tests, and deductive thematic analyses were performed. Thirty-three participants (53.7 ± 14.9 years-of-age) showed statistically significant gains in transportation self-efficacy (p < 0.01) and satisfaction with bus use (p < 0.01), with no statistically significant differences in mobility (p > 0.05). Qualitative findings confirmed enhanced transportation self-efficacy and satisfaction with bus use, with participants reporting using buses to carry out some daily activities. The P-SAMI shows promise for improving transportation self-efficacy and satisfaction with using the bus, with the potential to enhance participation in daily activities. Controlled trials should be conducted in the future to test the effectiveness of transportation training for people with disabilities.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".