Ageing in deprived neighbourhoods
Bibliographic record
Abstract
Introduction This chapter presents the results and analysis of face-to-face interviews conducted with older people living in deprived urban neighbourhoods of Manchester, England, and Vancouver, Canada. The aim of the chapter is to better understand some of the factors that underpin older people's desire to age in place despite multiple risks presented in their environment and conversely why others reject ageing in place. The analysis begins with a summary of the key characteristics of the research participants in the two cities. This profiles the sample in relation to such factors as gender, age, marital status, ethnicity, health status, housing tenure, educational achievement and rating of quality of life. The discussion then focuses on the overall environmental expression of older people, which is found to fall into three categories – environmental comfort, environmental management and environmental distress . To illustrate the environmental categories, the chapter presents an in-depth examination and discussion of eight case studies, four from Manchester and four from Vancouver. Each case study presents a detailed narrative of the participant's relationship with place, highlighting daily activities, social support, health status, financial concerns, feelings towards neighbourhood and well-being. Following each case study is an analysis and discussion of the issues and themes raised. Sample characteristics The sociodemographic characteristics of the 52 participants were broadly comparable between Manchester and Vancouver (see Appendix A). At ward level the three Manchester wards – Cheetham, Longsight and Moss Side – and the Vancouver ward of Grandview-Woodland shared the most similarities with respect to sociodemographic factors. However, the sample in Vancouver's DES differed with respect to gender, marital status, number of reported health problems, income and rating of quality of life. Many of these characteristics are reflective of people living in the area. Overall, the sample had almost equal numbers of men and women. In all of the wards, except the DES, women outnumbered men. Conversely, in the DES, 14 of the 19 interviews conducted were with men, reflecting a predominance of men in the area (see area profile for the DES in Chapter Four). The mean age of the two samples were broadly similar; the Manchester sample had a mean age of 70, while the Vancouver sample had a mean age of 73. Both samples also reflected the ethnic diversity of the study areas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".