The Influence of Poverty and Well Being of the Elderly People in Nyanza Province, Kenya
Bibliographic record
Abstract
Kenya’s population aged 60+ is estimated to be 1.8 million, that is, 9% of the total population and is projected to increase to about 2.2 million by 2012. This raises questions as to the socio-economic situation and well-being of the older population given the prevailing economic conditions and decreasing family sizes occasioned by family planning and the migration of the youth to urban areas in search of employment. A descriptive study using quantitative survey questionnaires, qualitative interviews and observation checklist was conducted among a sample of 120 older men and women aged women aged 60+ in three districts in Nyanza province. The majority (57%) of older people earned incomes of less than Ksh. 2000 (US $25), older people’s major source of income was from small-scale growing and selling of vegetables, eggs, milk, and fruits. The majority of the sample (64%) had only completed primary education, 68% had low food nutrient intake, 66.7% hypertension, 13% diabetes, 73% joint aches, 22% suffered from HIV/AIDS, 29% were affected by HIV/AIDS, 77.5 of women had menopause related discomforts, and 19.2% of men had prostate problems. Although 82% had geographical access to health facilities, services were experienced as unaffordable or inadequate. In conclusion, older people’s poverty produces vulnerability to malnutrition and untreated degenerative diseases. Dependence on help from children and well-wishers is older people’s main, but inadequate, resource for trying to cope with this vulnerability. Despite formal government commitment, concrete policies to ensure the economic well-being of older people are absent. National level research to establish the nature and determinants of older people’s socio-economic situation is needed to promote and inform such policy development.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".