Intergenerational Community in Multi-Unit Housing: A Strategy to Address Ageism
Bibliographic record
Abstract
Intergenerational contact is a strategy found to redress ageism and age-segregation in geographic places. The scholarly literature is primarily focused on structured intergenerational (IG) programs in communities and neighbourhoods, with limited studies of informal IG community in a residential setting inclusive of people of all ages. This study explored how intergenerational contact is seeded, nourished and sustained in the context of a high-rise building, and the structures required to facilitate organic intergenerational community. It responds to a knowledge gap on the process and experiences of age integration in IG housing, and the exclusion of young and middle-aged adults in research in the field. This thesis reports on a qualitative study using community based participatory action research (CBPAR) methodology. The research site was a high-rise condominium building in Toronto, Ontario with nine years of sustained IG initiatives. There were nine research participants ranging from 10 to 80+ years. One focus group was conducted and after a preliminary thematic data analysis (Braun and Clarke, 2006), initial themes were shared with participants in a follow-up research feedback session as a prompt for further discussion. Analysis of the transcripts generated five key themes: foundational structures; growing and cultivating IG community; benefits and outcomes of IG community; the roles of generational cohorts; and residents of the building who are missing from IG community. Participants highlighted the psychosocial benefits of IG community for individuals, such as community and volunteer engagement, sense of community and caring, and social skills development, and the socio-environmental benefits for the community, such as respect and care for common areas and a greater acceptance of diversities. Consistent with the literature, young adults were missing from this IG community. This study contributes to the current social gerontological literature on intergenerational practice and housing, with a focus on organic IG development and age integration. The results can inform future research exploring social connectedness and belonging with contributions to the empirical, methodological, and theoretical literature on IG practice and community.
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.003 | 0.039 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".