178 Exploring mobility patterns and social assets: informing housing decisions for Canadian older adults
Bibliographic record
Abstract
Introduction Decision aids about housing options for older adults overlook mobility patterns and social assets. We explored how Canadian older adults’ home mobility patterns and social health could better inform housing decisions. Methods In accordance with GRAMMS, we conducted a longitudinal study in Quebec and Alberta with older adults (aged 65 or older) living independently and able to walk. We collected data on mobility and social health as well as sociodemographics, health status and quality of life. Participants documented their mobility patterns for 14 days through a GPS tracker and daily journal, capturing details on destinations, activity purpose, length, type, frequency, duration, and weather. Walking and in-depth interviews offered insights into physical and social assets and obstacles to social health and mobility. Self-administered questionnaires assess sociodemographics, health status and quality of life. Triangulation enriched our findings qualitatively. Lastly, we selected four contrasting participants for activity space maps (2 in Quebec and 2 in Alberta), interpreting GPS data alongside other sources, contributing nuanced perspectives. Results Of 25 approached, 20 participated, 70% female; mean age 80.4; mean years living in the same neighborhood 15.6 (±7.9). Fourteen used GPS trackers, 9 correctly. All engaged in 4 other data collections: GPS maps showed trips, mainly by car (n=9) and walking (n=5). Two participants also combine the bus in their means of transportation. Daily journals revealed mainly solo travel (n=6). Interviews emphasized physical assets, like libraries and supermarkets (n=10), and notable social assets, such as desired family support (n=13) and neighborhood intimacy (n=14). Winter significantly impacted outdoor activities (n=13). Discussion Canadian older adults’ home mobility patterns and social health provide useful information that could improve housing decision-making. Conclusion Our findings show that mobility patterns are important to inform decisions about housing options.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".