Measuring walkability and bikeability for health equity and intervention research: a scoping review
Bibliographic record
Abstract
The purpose of this study was to describe self-report and audit-based measurement tools of neighbourhood walkability and bikeability for health equity and intervention research. We conducted a scoping review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. We searched MEDLINE via PubMed, Embase, Web of Science, and SPORTDiscus with full text via EBSCO in March 2022. We extracted data from a total of 35 papers which reported on 23 self-report and 15 audit-based measures assessing walkability and bikeability. Studies spanned multiple regions including Africa, America, Australia, and Europe, but most were conducted in the United States (n = 15), followed by Australia (n = 6). The most used self-report measure was the Neighbourhood Environment Walkability Scale (NEWS), while the audit tools Pedestrian Environment Data Scan and Bridge the Gap Street Segment Tool were each used in two studies. The priority populations most often studied were residents of low socio-economic status/high disadvantage neighbourhoods, racialized groups, women, youth, older adults, and rural populations. Ultimately, there is no one tool that can be recommended for use in all contexts and with all priority populations; rather, tools may require adaptations to specific contexts and populations of interest.
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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 | 0.040 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| 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".