Common measures of green and blue space for built environment, health equity and intervention research: a scoping review
Bibliographic record
Abstract
The purpose of this study was to describe self-report and audit-based measurement tools of green and blue space used for health equity and intervention research. This scoping review was conducted and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). In March 2022, we performed a literature search of MEDLINE, Embase, Web of Science, and SPORTDiscus. We found 22 papers, six of which used self-report tools and 16 of which relied on audit-based measures to assess green or blue space. These tools measure aspects of blue and green space including accessibility, equipment, and use. The System for Observing Parks and Recreation in Communities (SOPARC) was most used followed by the Public Open Space Audit Tool (POST) and the Community Park Audit Tool (CPAT). The priority populations most often studied were residents of low socio-economic status/high disadvantage neighbourhoods, followed by racialized groups and women. This scoping review provides guidance on common measurement tools that can be used by researchers working on green/blue space for health equity and intervention research. No reliable and valid self-report measure was used or available in the literature to examine equity in green/blue space.
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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 | 0.067 | 0.194 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.033 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 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".