A Walk-Along Study: Exploring Older Adults’ Perspectives on Age-Friendly Features and Playfulness in Ottawa’s Public Spaces
Bibliographic record
Abstract
Many cities are experiencing a profound demographic shift, with individuals aged 65 and above increasingly outnumbering younger populations. While longevity comes with many benefits, there is increasing evidence of the physical and social challenges associated with aging in urban settings. One neglected area of study is the role of play in addressing some of the challenges related to aging. Despite its recognized benefits for social connectedness and holistic health, play remains an overlooked concept in urban planning for older populations, particularly those from ethnically diverse backgrounds. This study examines how and to what extent play can enrich the lives of Canada’s aging population. Using Ottawa as a case study, the research employed 14 walk-along interviews with participants aged 65 and above. The research followed three core objectives: (1) to evaluate age-friendly features of Ottawa’s publicly funded play spaces, (2) to examine older adults’ lived perspectives of play and socialization, with a focus on ethnically diverse older adults, and (3) to develop actionable planning and policy recommendations that advance inclusivity and expand play opportunities. The study concludes with five policy recommendations to support equitable, play-friendly environments: (1) recognize play within social determinants of health frameworks, (2) invest in culturally responsive public spaces, (3) expand intergenerational programming, (4) improve transit and mobility infrastructure, and (5) reconceptualize walking as a form of playful engagement. The report’s insights inform and advance emerging age-friendly planning research, providing actionable strategies for enhancing social inclusion and healthy aging in Canadian cities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".