Understanding the barriers and facilitators of urban greenway use among older and disadvantaged adults: A mixed-methods study in Québec city
Bibliographic record
Abstract
Urban greenways are multipurpose and multi-user trails that provide a range of socio-ecological and health benefits, including active transportation, social interactions, and increased well-being. However, despite their numerous benefits, barriers exist that limit urban greenway access and use, particularly among older and disadvantaged adults. This study addresses a significant research gap by examining the nuanced factors that influence the choices and experiences of these specific user groups in Québec City, Canada. We use a mixed-methods' approach to explore the facilitators of and barriers to access and use of two urban greenway trails among older and disadvantaged adults. Our methods included a greenway user count, 96 observation time slots, and 15 semi-structured user interviews. The results revealed significant use of greenway trails by older adults for afternoon walks in both seasons studied (autumn and winter). We also observed variations in use patterns, such as higher levels of solitary walking, reduced levels of winter cycling, and the impracticality of the secondary greenway trail owing to snow conditions. In addition, the findings revealed a wide range of factors that influence greenway access and use, categorized as individual or personal, physical or built environment, social environment, and meteorological or climatic dimensions. Future research can build on these insights to design and assess interventions that capitalize on the facilitators and address any barriers, enhancing the value of urban greenways for older and disadvantaged adults.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".