Influence of public transport training for people with disabilities: Protocol for a systematic review
Bibliographic record
Abstract
• Travel training is a potential solution enable PWD to improve knowledge, psychosocial skills and public transportation use. • Traveling independently using a public transit is an expression of autonomy and can facilitate social interactions. • It is therefore up to transport operators to implement travel training programs based on scientific evidence to better meet travel needs of PWD. • Our systematic review will identify best practices in travel training and provide information on how to address the travel training needs of PWD. Travel training programs that include components of the real environment, simulation, application, and virtual reality may facilitate increased use of fixed-route public transport among people with disabilities (PWD). Existing evidence on travel training programs focused on outcomes in youth with disabilities. However, little is known about how such programs may influence knowledge and psychosocial skills for public transport use among adult with disabilities and older adults. This systematic review aims to examine the influence of travel training on knowledge, psychosocial skills (e.g., transportation-related self-efficacy and self-determination), and public transport use among adult with disabilities and older adults. This protocol of systematic review will be guided by the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) 2015. The literature search will include empirical studies published in French and English from 1990 to 2023 in the following database: MEDLINE, PsycINFO, and Transport Database via Ovid platform, and Embase and the Web of science. An update will be made to ensure that all relevant material is retrieved. All literature search results will be imported in Covidence software for screening and data extraction. Given that we expect to have studies with a variety of data collection methods and tools, the meta-analysis will not be used. The results of this review will be reported according to PRISMA 2020. A review of the influence of travel training on various outcomes will facilitate transport operators to develop and implement the best travel training interventions to better meet public transit needs of PWD.
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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 | 0.069 | 0.093 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.020 | 0.020 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.090 | 0.009 |
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".