A Direct Assessment of “Obesogenic”Built Environments: Challenges and Recommendations
Bibliographic record
Abstract
This paper outlines the challenges faced during direct built environment (BE) assessments of 42 Canadian communities of various income and urbanization levels. In addition, we recommend options for overcoming such challenges during BE community assessments. Direct BE assessments were performed utilizing two distinct audit methods: (1) modified version of Irvine-Minnesota Inventory in which a paper version of an audit tool was used to assess BE features and (2) a Physical Activity and Nutrition Features audit tool, where the presence and positions of all environmental features of interest were recorded using a Global-Positioning-System (GPS) unit. This paper responds to the call for the need of creators and users of environmental audit tools to share experiences regarding the usability of tools for BE assessments. The outlined BE assessment challenges plus recommendations for overcoming them can help improve and refine the existing audit tools and aid researchers in future assessments of the BE.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".