Priorities and Indicators for Economic Evaluation of Built Environment Interventions to Promote Physical Activity
Bibliographic record
Abstract
BACKGROUND: Built environment approaches to promoting physical activity can provide economic value to communities. How best to assess this value is uncertain. This study engaged experts to identify a set of key economic indicators useful for evaluation, research, and public health practice. METHODS: Using a modified Delphi process, a multidisciplinary group of experts participated in (1) one of 5 discussion groups (n = 21 experts), (2) a 2-day facilitated workshop (n = 19 experts), and/or (3) online surveys (n = 16 experts). RESULTS: Experts identified 73 economic indicators, then used a 5-point scale to rate them on 3 properties: measurement quality, feasibility of use by a community, and influence on community decision making. Twenty-four indicators were highly rated (≥3.9 on all properties). The 10 highest-rated "key" indicators were walkability score, residential vacancy rate, housing affordability, property tax revenue, retail sales per square foot, number of small businesses, vehicle miles traveled per capita, employment, air quality, and life expectancy. CONCLUSION: This study identified key economic indicators that could characterize the economic value of built environment approaches to promoting physical activity. Additional work could demonstrate the validity, feasibility, and usefulness of these key indicators, in particular to inform decisions about community design.
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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 | 0.241 | 0.284 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".