Ageing, urban marginality, and health in Ghana
Bibliographic record
Abstract
The world’s population is rapidly ageing. Global estimates for the next three decades indicate a two-fold increase in the population of older adults aged ≥60 years. Nearly 80% of this growth will occur in low and middle-income countries in Asia and sub-Saharan Africa, where population health is already under threat from poverty, degraded environments, and deficient healthcare systems. Although the world’s poorest region, sub-Saharan Africa, ironically, will witness the fastest growth in older populations, rising by 64% over the next 15 years. Indications are that the majority of this population will live in resource-poor settings, characterized by deficient housing and neighbourhood conditions. Yet, very little research has systematically examined the health and wellbeing of older adults in such settings. Drawing on the ecological theory of ageing, the present study explores the living conditions and quality of life of elderly slum dwellers in Ghana, a sub-Saharan African country with a growing population of older adults. Data collection was undertaken in two phases in two environmentally contrasting neighbourhoods in Accra, Ghana. In Phase 1, we carried out a cross-sectional survey of older adults in a slum community (n = 302) and a non-slum neighbourhood (n = 301), using the World Health Organization quality of life assessment tool (WHOQoL-BREF). The survey data were complemented in Phase 2 with qualitative interviews involving a sample of community dwelling older adults (N = 30), health service providers (N = 5), community leaders (N = 2), and policymakers (N = 5). Preliminary analysis of the survey data revealed statistically significant differences in the social and environment domains of quality of life, while the qualitative data identified multiple health barriers and facilitators in the two neighbourhoods. Insights from the research are expected to inform health and social interventions for older slum dwellers in Ghana.
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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 | 0.001 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".