Social participation needs of older adults in an urban revitalization: results from participatory action research during the COVID-19 pandemic
Bibliographic record
Abstract
Adults aged 80 or older, with a low income, with a disability, or belonging to ethnic, linguistic, sexual or gender minorities are particularly at risk of exclusion, especially during neighborhood revitalizations and pandemics. This study aimed to document individual and collective needs, facilitators and barriers to social participation of older residents and users of downtown Sherbrooke (Quebec, Canada) at risk of marginalization during an urban revitalization. We used a participatory action research design with 32 older adults, 1 caregiver and 5 community partners. Participants expressed high expectations regarding the downtown revitalization. Individual and collective social participation needs were identified, particularly relating to inclusive environments, i.e. adapted, safe, clean and healthy; with access to activities, resources, affordable transportation and housing; accompanied to participate in activities; and informed about social participation opportunities. Main facilitators were health, income, public space designed to promote social interaction, activities offered, assistance of family and friends, and security. Obstacles were disabilities, precarious living conditions, COVID-19 restrictions and discrimination. The pandemic made collaborative research difficult, which triggered new strategies (e.g. media watch, research newsletter). Results could improve the development of a downtown area, inclusive for all older adults, and inspire future projects to leverage the power of 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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".