Getting Old Well in Sub Saharan Africa: Exploring the Social and Structural Drivers of Subjective Wellbeing among Elderly Men and Women in Uganda
Bibliographic record
Abstract
While literature attempts to explain why self-reported subjective wellbeing (SWB) generally increases with age in most high-income countries based on a social determinants of a health framework, little work attempts to explain the low levels of self-report SWB among older persons in sub-Saharan Africa. Using the 2013 Uganda Study on Global Aging and Health with 470 individuals, this research examines (i) direct and indirect effects of age on SWB through social and structural determinants, and (ii) how direct and indirect effects vary by gender. Results show a significant direct and negative effect of age on SWB (β = 0.42, p = 0.01). Six indirect paths were statistically significant and their indirect effects on wellbeing varied by gender. Providing support, education, working status, asset level, financial status and financial improvement were significantly positively associated with men’s SWB, whereas younger age, providing community support, participating in group activities, number of close friends/relatives, government assistance and all socio-economic variables were significantly positively associated with women’s SWB. Strategies to address gendered economic, social and political inequalities among and between elderly populations are urgently needed.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".