A Novel Method to Evaluate the Community Built Environment Using Photographs – Environmental Profile of a Community Health (EPOCH) Photo Neighbourhood Evaluation Tool
Bibliographic record
Abstract
BACKGROUND: Previous research has shown that environments with features that encourage walking are associated with increased physical activity. Existing methods to assess the built environment using geographical information systems (GIS) data, direct audit or large surveys of the residents face constraints, such as data availability and comparability, when used to study communities in countries in diverse parts of the world. The aim of this study was to develop a method to evaluate features of the built environment of communities using a standard set of photos. In this report we describe the method of photo collection, photo analysis instrument development and inter-rater reliability of the instrument. METHODS/PRINCIPAL FINDINGS: A minimum of 5 photos were taken per community in 86 communities in 5 countries according to a standard set of instructions from a designated central point of each community by researchers at each site. A standard pro forma derived from reviewing existing instruments to assess the built environment was developed and used to score the characteristics of each community. Photo sets from each community were assessed independently by three observers in the central research office according to the pro forma and the inter-rater reliability was compared by intra-class correlation (ICC). Overall 87% (53 of 60) items had an ICC of ≥ 0.70, 7% (4 of 60) had an ICC between 0.60 and 0.70 and 5% (3 of 60) items had an ICC ≤ 0.50. CONCLUSIONS/SIGNIFICANCE: Analysis of photos using a standardized protocol as described in this study offers a means to obtain reliable and reproducible information on the built environment in communities in very diverse locations around the world. The collection of the photographic data required minimal training and the analysis demonstrated high reliability for the majority of items 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".