Systematic Review of Active Travel to School Surveillance in the United States And Canada
Bibliographic record
Abstract
Active travel to school is one way youths can incorporate physical activity into their daily schedule. It is unclear the extent to which active travel to school is systematically monitored at local, state, or national levels. To determine the scope of active travel to school surveillance in the US and Canada and catalog the types of measures captured, we conducted a systematic review of peer-reviewed literature documenting active travel to school surveillance published from 2004 to February 2018. A study was included if it addressed children's school travel mode across two or more time periods in the US or Canada. Criteria were applied to determine whether a data source was considered an active travel to school surveillance system. We identified 15 unique data sources; 4 of these met our surveillance system criteria. One system is conducted in the US, is nationally representative, and occurs every 5-8 years. Three are conducted in Canada, are limited geographically to regions and provinces, and are administered with greater frequency (e.g., 2-year cycles). School travel mode was the primary measure assessed, most commonly through parent report. None of the systems collected data on school policies or program supports related to active travel to school. We concluded that incorporating questions related to active travel to school behaviors into existing surveillance systems, as well as maintaining them over time, would enable more consistent monitoring. Concurrently capturing behavioral information along with related environmental, policy, and program supports may inform efforts to promote active travel to school.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.030 | 0.040 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".