Nature for All? Assessing Equitable Public Access to National Urban Parks in Canada: A Comparative Analysis
Bibliographic record
Abstract
The number of health challenges is rising both globally and in Canada. Research consistently shows that time spent in nature can mitigate some of these health challenges, offering significant mental, physical, and well-being benefits. Parks and protected areas in Canada, including the expansion of the National Urban Park (NUP) program by Parks Canada, provide valuable opportunities for nature-based engagement. However, access to these spaces is unjust, as many individuals face structural, intrapersonal, and interpersonal constraints. While prior studies have explored the motivations and well-being outcomes of women and Black, Indigenous, and People of Colour (BIPOC) individuals, there remains a limited understanding of the constraints they face in accessing parks and protected areas. Additionally, limited research has examined the constraints experienced by parents and their children. This study analyzed data from a Canadian household survey conducted by ParkSeek, using SPSS v28. The sample included 227 respondents from Whitchurch-Stouffville (located near the existing Rouge NUP) and Victoria (site of a proposed NUP). The analysis examined how ten sociodemographic variables – particularly gender identity, ethnic background, and family structure – influenced motivations, perceived health and well-being outcomes, and constraints to accessing parks and protected areas. Four important findings emerged: (1) respondents were significantly motivated by group togetherness and perceive strong social well-being benefits; (2) women in Whitchurch-Stouffville experienced more structural and intrapersonal constraints than men, while women in Victoria faced greater constraints than men across all three constraint categories; (3) BIPOC respondents at both sites reported higher levels for all three constraint categories compared to white/Caucasian respondents; and (4) parents and children aged under 18 were constrained by a lack of time and competing preferences. These findings advance knowledge on motivations, health and well-being outcomes, and constraints in the context of parks and protected areas. They can help encourage individuals to visit parks and protected areas for health and well-being benefits, while also informing Parks Canada of recommendations regarding access to these areas, including existing and proposed NUPs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".