Living with dementia during COVID-19: a participatory examination of suburban neighbourhood access and place
Bibliographic record
Abstract
The World Health Organization identifies dementia as the leading cause of dependency and disability among older adults. Nearly 2/3 of people living with dementia in Canada live in community. Continued access to neighbourhoods and amenities has many benefits: improved mental/physical health, more social interaction, and sense of worth and dignity. The COVID-19 pandemic has altered our access to amenities, physical activity levels and social interactions, and it is more acute for people living with dementia – increased rates of social isolation, fear, and anxiety due to closure of social programs/activities, and interruptions in daily routines and social networks. COVID-19 has also disproportionately impacted our socio-spatial peripheries, prompting pleas to study the pandemic from these locations instead of city centres. This exploratory case study examines the mobility and socio-spatial caring relations of four community-dwelling people living with dementia in Oshawa, Canada (a suburban municipality east of Toronto) during the pandemic to understand wellbeing impacts, using multiple participatory methods. This paper documents their experiences, including changes in mobility practices/destinations, built/social environment barriers, impacts of pandemic restrictions on wellbeing and everyday practices, concern for/caring about others. We end with recommendations for land use, transportation, parks, and emergency planners and community program providers to build more dementia-inclusive communities.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.012 |
| 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".