Urban Green Spaces and Social Cohesion in Racially and Ethnically Diverse Communities: A Scoping Review of Mediators, Inequities, and Health Equity Implications
Bibliographic record
Abstract
Background A predictor of public health, social cohesiveness may be improved by urban green areas through processes such as trust, affiliation, and social capital. However, these advantages are frequently prevented by inequitable access, maintenance discrepancies, safety concerns, and the threat of green redevelopment in racially and ethnically diverse neighborhoods.Objectives/Aims To collect information regarding the correlation between unity in society and urban green spaces across racially and ethnically diverse groups, while also identifying mediating variables, inequalities, and implications for public health equality.Methods A scoping review was performed in line with the PRISMA extension for scoping reviews. From the beginning until March 2024, MEDLINE, Scopus, Web of Science, and Embase were searched using a limited vocabulary and terms relating to natural space, social cohesion, and varied communities. The research was eligible if they assessed urban green areas and social integration, demonstrated linked health consequences, and looked into historically disadvantaged populations. Quantitative and qualitative designs were equally acceptable. Data extraction and quality evaluation (Newcastle-Ottawa Scale, CASP) were carried out in triplicate. A theme synthesis was created by combining data from six domains: the social environment, the built environment, leisure, maintenance, safety, and green gentrification.Results/Key Findings 126 studies met the inclusion criteria, which included a variety of demographics and geographic locations, out of 3,246 documents. High-quality, culturally inclusive, and easily accessible green spaces were consistently associated with improved mental health, increased physical activity, and increased community trust. Felt safety and leisure inclusion were the primary mediators. The advantages were restricted by structural imbalances, such as unequal park financing, biased implementation, and a lack of canopy cover. Green gentrification has a mixed effect, as it can occasionally improve amenities while also increasing the likelihood of exclusion and displacement. Causal inference was restricted by the variability in measurement and cross-sectional dominance.Conclusions/Implications Urban green spaces have the potential to improve the health and social cohesion of various communities; however, their benefits are contingent upon their cultural significance, maintenance, safety, and equitable design. It is essential that policymakers, practitioners, and academics work together to establish criteria for safety and cohesiveness, incorporate anti-displacement strategies, and collaborate to develop solutions with impacted communities. Optimizing the health equity advantages of urban greening necessitates the mitigation of structural imbalances.
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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 | 0.016 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.019 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".