ASSESSMENT OF AGE AND DEMENTIA FRIENDLINESS OF OTTAWA COMMUNITIES AS PERCEIVED BY PERSONS WITH DEMENTIA AND CARE PARTNERS
Bibliographic record
Abstract
Abstract In Canada there are over 500,000 persons living with dementia with prevalence estimates reaching as high as 912,000 by the year 2030. Given that age is the strongest known predictor of dementia and the fact that our population is ageing, there is an urgent need to create communities that promote older adults (including those living with dementia) reaching their maximum potential and feeling welcomed and included while ageing in place. The aim of our study was to determine the utility of a tool developed using a citizen science approach with persons living with dementia and their care partners to determine how they perceive the age- and dementia-friendliness of their neighbourhoods (where they live, work, conduct business and socialise). Ten participants were recruited for a pilot study which took place over a six-week period. The project designed and tested an audit tool, accessed via a smart phone/tablet, that allowed data to be collected quickly and in real time. This audit tool also allowed participants to upload quantitative and qualitative responses (including photos of locations being audited). Participants were trained to become citizen scientists in a series of workshops where they also collaborated with researchers to develop the audit tool. During the data collection period, citizen scientists audited locations/spaces that they visited during their day and submitted their responses using the app. Our findings present a case for increased inclusion of older adults, including those living with dementia, in research and intervention programs that target the promotion of age- and dementia-friendly 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".