‘We are developing our bubble’: role of the built environment in supporting physical and social activities in independent-living older adults during COVID-19
Bibliographic record
Abstract
This study explores how the built environment can support and challenge a bubble strategy designed to protect older adults from virus transmission while at the same time allowing them maintain their physical and social activities during COVID-19. We conducted a case study of older adults in an independent-living building and the surrounding neighborhood in Edmonton, Alberta, Canada. Data were collected through building and neighborhood observations, and 11 semi-structured in-depth interviews with 6 building residents and 6 stakeholders. Data were analyzed through mapping and interpretative phenomenological analysis (IPA). Complex and nuanced relationships between human and nonhuman factors that supported and challenged the bubble are elaborated in three built environment categories. (1) ‘Building interiors’, where residents conduct routine activities and attend physical and social activities with neighbors, were central to the bubble. (2) ‘Neighborhood environments’ were extensions of the bubble that affected residents’ outdoor activities. (3) ‘Building edges’ were important for balancing residents’ needs for connecting to the world outside and protecting themselves from the virus. Communities should consider the bubble strategy combined with built environment supports to assist older adults in protecting themselves against virus transmission, and maintaining physical and social activities during the ongoing pandemic and future epidemics.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| 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".