ASSESSING THE RURAL BUILT ENVIRONMENT TO SUPPORT OLDER ADULTS MOBILITY AND SOCIAL INTERACTION
Bibliographic record
Abstract
The built environment plays an important role in supporting older adults to successfully age in place. The land-use patterns, transportation systems and community design elements that together comprise the built environment all directly affect how older adults move and interact within a community. Older adults who live in built environments with physical barriers are less likely to leave their homes and therefore are more at risk of social isolation, reduced physical activity, and increased mobility issues which can affect their ability to successfully age in place. To date, most of the research on the influence of the built environment has focussed primarily on urban settings with little understanding of the application to older adults in rural settings. Our presentation will focus on the adaptation of community audit instruments to assess the built environment in four rural communities with small populations in the province of Saskatchewan, Canada. We will present findings from a study where we used three methods to assess the rural built environment: community audits using the Healthy Aging Network (HAN) environmental audit tool, local policy assessments using the Rural Active Living Assessment (RALA) tool and focus groups with community dwelling older adults. We will discuss our methods of adapting these instruments for use in small rural communities, will highlight our use of mapping technology to summarize findings and discuss the contribution of these findings to local community governments who are formalizing their age-friendly initiatives.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".