From social network to safety net: Dementia-friendly communities in rural northern Ontario
Bibliographic record
Abstract
Dementia-friendly communities, as communities that enable people with dementia to remain involved and active and have control over their lives for as long as possible, centrally involve social support and social networks for people living with dementia. The purpose of this research was to explore and understand the context of dementia in rural northern communities in Ontario with an emphasis on understanding how dementia friendly the communities were. Using qualitative methods, interviews were conducted with a total of 71 participants, including 37 health service providers, 15 care partners, 2 people living with dementia and 17 other community members such as local business owners, volunteers, local leaders, friends and neighbours. The strong social networks and informal social support that were available to people living with dementia, and the strong commitment by community members, families and health care providers to support people with dementia, were considered a significant asset to the community. A culture of care and looking out for each other contributed to the social support provided. In particular, the familiarity with others provided a supportive community environment. People with dementia were looked out for by community members, and continued to remain connected in their communities. The social support provided in these communities demonstrated that although fragile, this type of support offered somewhat of a safety net for individuals living with dementia. This work provides important insights into the landscape of dementia in rural northern Ontario communities, and the strong social supports that sustain people with dementia remaining in the 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".