Validation of desk-based audits using Google Street View® to monitor the obesogenic potential of neighbourhoods in a pediatric sample: a pilot study in the QUALITY cohort
Bibliographic record
Abstract
BACKGROUND: to assess street- and neighbourhood-level features relevant to pediatric obesity (QUALITY-NHOOD tool, the test method) and (ii) comment on its capacity to detect changes in the built environment over an 8-year period. In order to do so, we compared this tool with an on-site auditing instrument (the reference method). METHODS: On-site audits of 55 street- and neighbourhood-level features were completed in 2008 in 512 neighbourhoods from the QUALITY cohort study. In 2015, both repeat on-site and desk-based audits were completed in a random sample of 30 of these neighbourhoods. RESULTS: Agreement between both methods was excellent for almost all street segment items (range 91.9-99.7%), except for road type (81.0%), ads/commercial billboards (81.7%), road-sidewalk buffer zone (76.1%), and road-bicycle path buffer zone (53.3%). It was fair to poor for perceived quality, safety and aesthetics items (range 59.9-87.6%), as well as for general impression items (range 40.0-86.7%). The desk-based method over-detected commercial billboards and road-sidewalk buffer zone, and generally rated neighbourhoods as less safe, requiring more effort to get around, and having less aesthetic appeal. Change detected over the 8-year period was generally similar for both methods, except that the desk-based method appeared to amplify the increase in the number of segments with signs of social disorder. CONCLUSIONS: The QUALITY-NHOOD tool is deemed adequate for evaluating and monitoring changes in pedestrian- and traffic-related features applicable to pediatric populations. Applications for monitoring the obesogenic nature of neighbourhoods appear warranted.
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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 | 0.022 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".