Older adults' perceptions of age-friendly communities in Canada: a photovoice study
Bibliographic record
Abstract
ABSTRACT The concept of age-friendly communities has garnered international attention among researchers, policy makers and community organisations since the World Health Organization launched its Global Age-friendly Cities Project in 2006. Despite the growth of the age-friendly communities movement, few studies have examined age-friendly characteristics within different community contexts. The goal of the present study was to use a participatory methodology to explore older adults’ perceptions of age-friendliness. The study employed the photovoice technique with 30 community-based older adults in one urban community and three rural communities in the province of Manitoba, Canada. Participants were provided with cameras and took photographs to illustrate the relative age-friendliness of their communities and to generate discussion in interviews and focus groups. Themes from photographs, interviews and focus groups were organised into three broad categories: age-friendly features, contextual factors and cross-cutting themes. The age-friendly features we identified in this study generally correspond to the World Health Organization domains of age-friendliness. In addition, we identified three contextual factors that impact the experiences of older adults within their community environment: community history and identity, ageing in urban, rural and remote communities, and environmental conditions. Finally, independence, affordability and accessibility were identified as cross-cutting themes that intersect with various community features and contextual factors.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".