Key age-friendly components of municipalities that foster social participation of aging Canadians: results from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Abstract Municipalities can foster the social participation of aging adults. Although making municipalities age-friendly is recognized as a promising way to help aging adults stay involved in their communities, little is known about the key components (e.g., services and structures) that foster social participation. This study thus aimed to identify key age-friendly components (AFC) best associated with the social participation of older Canadians. Secondary analyses were carried out using baseline data from the Canadian Longitudinal Study on Aging (n=25,411) in selected municipalities (m=110 with ≥30 respondents), the Age-friendly Survey, and census data. Social participation was estimated based on the number of community activities outside the home per month. AFC included housing, transportation, outdoor spaces and buildings, safety, recreation, workforce participation, information, respect, health and community services. Multilevel models were used to examine the association between individual social participation, key AFC, and environmental characteristics, while controlling for individual characteristics. Aged between 45 and 89, half of the participants were women who were engaged in 20.2±12.5 activities per month. About 2.5% of the variance in social participation was attributable to municipalities. Better outdoor spaces and buildings (p<0.001), worse communication and information (p<0.01), and lower material deprivation (p<0.001), were associated with higher social participation. Age was the only individual-level variable to have a significant random effect, indicating that municipal contexts may mediate its impact with social participation. This study provides insights to help facilitate social participation and promote age-friendliness, by maintaining safe indoor and outdoor mobility, and informing older adults of available activities.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".