Latin American immigrants in urban nature: Exploring mental health and belonging through walking interviews
Bibliographic record
Abstract
Urbanization is linked to mental health challenges, while urban nature is increasingly recognized for its restorative benefits. Yet, access to these benefits remains unequal. Latin American (LA) immigrants—a small but growing population in Canadian cities—may face distinct barriers to engaging with natural spaces, despite often relying on public resources for mental health and belonging. This study explores how urban nature contributes to well-being among LA immigrants in Vancouver, Canada. We conducted 30 semi-structured walking interviews in summer 2024 with participants who had lived in the city for one to ten years. Using reflexive thematic analysis, we examined how interactions with urban nature related to mental health and belonging, and the barriers that shaped these experiences. Nature supported mental health through five pathways: intrinsic qualities, meaningful activities, mindful engagement, personal reflection, and contrast with urban life. Belonging was fostered through engagement, cultural adaptation, and new traditions. However, structural and cultural barriers—including unfamiliar weather, limited prior nature experience, and exclusionary norms—restricted access and reduced benefits. Participants emphasized contemplative, reflective experiences over physical activity or socializing. Urban nature emerged as a space of negotiation where participants actively “tamed” unfamiliar environments to build connection and meaning—highlighting the need for inclusive, culturally responsive nature spaces.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".