Place attachment and sense of community in natural and built pedestrian spaces: an equity-informed systematic review
Bibliographic record
Abstract
One’s sense of community (SoC) and place attachments (PA) related to their local environments can have important consequences for their health. Professionals in planning and public health have sought to promote health by designing and leveraging pedestrian places to support SoC and PA. Existing reviews are yet to document how engagement with pedestrian places and designs may impact SoC and PA. This review i) collates the existing literature exploring the relationships between engagement with natural and built pedestrian places and designs and SoC/PA, and ii) examines the inclusion of health equity considerations (via Cochrane PROGRESS+ tool) related to distinct groups. Systematic searches of Scopus and Web of Science produced 7,874 records, from which 35 articles were ultimately included in the review synthesis. Generally, increased engagement with both built and natural pedestrian places/designs improved reported PA/SoC; however, as evaluated in our quality assessments, the general strength of evidence was limited due to cross-sectional designs, selection bias, and blinding issues. Additionally, natural places and designs were more frequently examined, outcome dimensions were disparately assessed (e.g. ‘shared emotional connections’ in SoC), and equity considerations were mostly focused on age (i.e. older samples) and socioeconomic status. Implications for research and practice are discussed.
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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 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".