Prevalence of Loneliness and Associated Factors among Older Adults in South Africa
Bibliographic record
Abstract
OBJECTIVE: Loneliness can be detrimental to health. The aim of this study is to estimate the prevalence of loneliness as well as its risk factors in older adults in South Africa.MATERIALS & METHODS: This cross-sectional population based study investigated factors associated with loneliness in a nationally representative sample (n=3624) of older South Africans who took part in the “Study of Global Ageing and Adults Health (SAGE)” wave 1 in 2008. The outcome variable was self-reported prevalence of loneliness and the exposure variables were socio-demographic characteristics and health variables.RESULTS: The overall prevalence of self-reported loneliness was 9.9%. Prevalence of loneliness was 10.2% for females and 9.5% for males, lowest among those married (7.5%), and highest among the 70+ years olds (12.5%). Individuals with highest level of education had the lowest prevalence of loneliness (5.9%). Indians or Asians were significantly more likely to experience loneliness than other population groups (Adjusted Odds Ratio=AOR: 3.20; 95% Confidence Interval=CI: 1.31, 7.80). Married or cohabiting individuals were significantly less likely to experience loneliness than unmarried or non-cohabiting ones, respectively (AOR: 0.55; 95% CI: 0.37, 0.81). In multivariable logistic regression, individuals with good subjective health were less likely to experience loneliness than those with poor health (AOR: 0.40, 95% CI: 0.22, 0.73). Similarly, individuals with good cognitive functioning were significantly less likely to experience loneliness than those with poor cognitive functioning (AOR: 0.55, 95% CI: 0.32, 0.97).CONCLUSION: The study found that the prevalence of loneliness among older adults in South Africa is significant. Preventative interventions that address the identified factors, including poor health status and low cognitive functioning, associated with loneliness need to be developed.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".