Vieillir chez soi dans la diversité des habitats de Montréal. Une opportunité d’innovation pour l’aménagement des quartiers ?
Bibliographic record
Abstract
Research Framework: The Age-Friendly Municipalities approach calls on Quebec municipalities to reflect on facilities and services that would allow their population to age inclusively and actively. The physical and functional attributes of the territory have a significant impact on the positive experience of aging in place. However, changes in the social mix of a neighborhood can add to these dimensions and impact the residential elders’ experience.Objectives: This article aims to explore the experience of aging in place in the context of strong demographic changes brought about by past and present immigration. It questions how immigration can change the dynamics of aging in place and lead to forms of innovation to be considered in the management of urban diversity.Methodology: A theoretical framework borrowed from environmental gerontology is used to analyze the interaction between seniors and the transformations of their living environment. A case study is proposed through focus groups conducted in 3 neighborhoods of the Montréal agglomeration marked by aging and immigration (Saint-Léonard, Cartierville, and Parc-Extension).Results: Aging in place in one's community is not a linear and stable experience. Population changes can lead to difficult residential experiences when the physical and functional configuration is not adapted to aging (Saint-Léonard), but also to positive experiences when it is more favourable (Cartierville, Parc-Extension). Structural demographic changes show the elders’ resilience in the face of a changing residential environment (Cartierville, Parc-Extension), as do the limits of their adaptation (Parc-Extension, Saint-Léonard).Conclusions: Aging in place in the context of immigration show even more that elderly people are not a homogeneous group. The results call for a more complex examination of the residential environment at the neighborhood level, particularly the notion of aging in place.Contributions: Neighborhoods can transform at a speed and in a dynamic where seniors from here and elsewhere can lose their grip. Theoretical models in environmental gerontology do not account for the dynamic nature of this scale of the home.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".