Children’s Access to Non-School Destinations by Active or Independent Travel: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Children's access to non-school destinations is important for their well-being, but this has been overlooked in transport planning. Research on children's access to non-school destinations is growing, and there is a need for a comprehensive overview, examining both quantitative and qualitative studies, of the existing evidence on places that children access by active or independent travel. OBJECTIVES: Identify and summarize quantitative and qualitative research on the topic of active or independent travel to non-school destinations for elementary aged children (6 to 13 years old). METHODS: Papers published in English between 1980 and July 2021 were sourced from: (i) Web of Science Core Collection; (ii) PubMed; and (iii) APA PsycInfo. Three relevant journals related to children and transport were hand searched: (i) Children's Geographies; (ii) Journal of Transport & Health; and (iii) Journal of Transport Geography. The search was limited to peer-reviewed articles published in English between 1980 and July 2021. Covidence, an online software platform for systematic reviews, was used to organize articles during the title and abstract screening stage. PRISMA-Scr is applied for reporting. RESULTS: 27 papers were retained from an initial 1293 identified peer-reviewed articles. The results reveal that children in different geographies travel unsupervised or by active modes to places that support different domains of their well-being such as a friend or relative's home, local parks or green spaces, recreational facilities, and different retail locations (e.g., restaurants). There is evidence that children's ability to reach certain places is constrained, likely due to safety concerns or environmental barriers. CONCLUSIONS: Research on children's diverse destinations is relatively limited as compared to trips to school. Various methodologies have been applied and can be combined to completement each other such as objective GPS tracking and subjective surveys on places children would go if they were available. Future research should clearly report and discuss the non-school destinations that children access to better inform transport planning and policy for all aspects of children's lives.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.022 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".