Gentrification and Chronic Conditions in Older Adults: Service Providers’ Perspectives
Bibliographic record
Abstract
Abstract Where we live impacts our health, but this is more apt for older adults (aged 55+) aging-in-place in their neighborhoods. Gentrification, i.e. the transformation of neighborhoods from low to high value, can put community-dwelling older adults at risk for residential displacement with limited retirement incomes and financial stressors like increased housing costs and property taxes, residential turnover and changing access to resources. As a place-based stressor, gentrification may exacerbate social vulnerabilities (e.g., lower socioeconomic status and racial/ethnic minority status) related to chronic condition (CC) disparities. But, little gentrification research focuses on these issues. This research examines associations between gentrification and older adults’ CC management related to broader social determinants in Hamilton, Ontario, Canada from health and social service providers’ perspectives. Hamilton, a recovering steel industry city with in-migration from Toronto, is experiencing higher costs of living, income inequality and tension with recent gentrifiers. I conducted key informant interviews with service providers in city government and community-based organizations using thematic analysis. Across providers, food insecurity, social isolation and displacement were the biggest issues associated with gentrification and CC, particularly for older adults with lower incomes and government disability support. Results thus far reveal Hamilton has numerous older adult-focused providers, but older adults often have difficulties accessing services due to a lack of knowledge, not always asking or realizing when they need help and coordinated referral difficulties across providers. To address these challenges, providers consider environmental scans, mapping resources and advertisement in an online community information database from the city’s public library.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".