Measuring walkability and bikeability for health equity and intervention research: a scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it