Agreement Between Virtual and In-the-Field Environment Audits of Assisted Living Sites
Bibliographic record
Abstract
The authors investigated the use of Google Earth's Street View option to audit the presence of built environment features that support older adults' walking. Two raters conducted virtual (Street View) and in-the-field audits of 48 street segments surrounding urban and suburban assisted living sites in metropolitan Vancouver, BC, Canada. The authors determined agreement using absolute agreement. Their findings indicate that Street View may identify the presence of features that promote older adults' walking, including sidewalks, benches, public washrooms, and destinations. However, Street View may not be as reliable as in-the-field audits to identify details associated with certain items, such as counts of trees or street lights; presence, features, and height of curb cuts; and sidewalk continuity, condition, and slope. Thus, the appropriateness of virtual audits to identify microscale built environment features associated with older adults' walking largely depends on the purpose of the audits-specifically, whether the measurer seeks to capture highly detailed features of the built environment.
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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 | 0.131 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".