Transportation to school and academic outcomes: a systematic review
Bibliographic record
Abstract
School transportation may have important implications for academic success, but few studies examine how these variables are related. This systematic review is the first that synthesizes research linking school transportation with academic outcomes. Using the PRISMA framework, the authors conducted a literature search across multiple data sources and screened articles for the following eligibility criteria: 1) Design is quantitative or mixed methods; 2) Sample consists of primary or secondary school students; 3) Variables include school transportation and academic outcomes. Two reviewers independently screened each study for inclusion criteria. Two reviewers also independently rated each study on methodological quality in five dimensions using the Mixed Methods Appraisal Tool (MMAT). Throughout the process, reviewers consulted to resolve any discrepancies between ratings. The search revealed 249 abstracts that were screened for inclusion criteria. This resulted in 61 relevant articles after duplicates were removed. After screening, reviewers determined that 26 studies met inclusion criteria. Most took place in the United States, although studies were also conducted in Canada, Croatia, England, Nepal, Pakistan, Portugal, South Africa, and Spain. Thirteen studies met all MMAT methodological quality standards. Longer travel times and transportation challenges were associated with adverse academic outcomes except when they provided access to higher-quality schools. School bus transportation had a mixed relationship with academic outcomes. The authors conclude that research points to the importance of transportation as it relates to academic success, especially for students of color and those from low-income households.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".