Evaluating the role of community-led urban greenspace programming in advancing belonging, social connection, and health equity: A mixed methods study of Park People's Sparking Change Toronto program
Bibliographic record
Abstract
Urban greenspaces have the potential to positively influence individual and population health and wellbeing. However, there are limited published evaluations of urban greenspace programming initiatives influencing social connection, belonging, and health equity, especially with equity-deserving and marginalized communities. This evaluation examined the impact of [organization and program name redacted], a national non-profit focused on urban greenspaces, on social connection, belonging, and health equity. An online survey was conducted in August and September of 2024 with 47 community members from the program along with four focus groups with 11 community members. Two additional focus groups were held with three City of Toronto staff and three [redacted] staff and two individual interviews were conducted with City of Toronto staff to accommodate schedules. We identified the importance of several factors including resourcing community-led urban greenspace programming, providing organizational support, championing community action, and addressing social access in fostering social connection, belonging, and health equity in urban greenspaces. Equity emerged as a central cross-cutting theme. This study presents three novel contributions to the literature on urban greenspaces and health: (1) the emphasis on taking a community-grounded, strengths-based approach to belonging and social connection; (2) a merged socio-ecological model (SEM) integrating both the material and social factors influencing belonging, social connection, and health equity, and (3) critical insights into the multi-scalar nature of barriers and facilitators and the importance of examining the dynamic interplay between levels and at different scales.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".