Sociodemographic, functional disability and severe illness predict extreme fatigue among older adults in Ghana
Bibliographic record
Abstract
<title>Abstract</title> Background Extreme fatigue is a disabling but under-recognized condition among older adults. Meanwhile, studies investigating impact of sociodemographic and health-related factors on extreme fatigue among older adults in Ghana are limited. This study, therefore, examined the prevalence and predictors of extreme fatigue among older adults in Ghana. Methods We analyzed cross-sectional data among community-dwelling older adults aged 50+ (N = 4,838) extracted from the 2023 Ghana Annual Household Income and Expenditure Survey (AHIES). Descriptive statistics were applied to estimate the prevalence of extreme fatigue. A multivariable model estimated adjusted associations, with significance at p < 0.05. Results Overall, 17.03% of participants reported experiencing extreme fatigue. In the multivariable model, severe illness (aOR = 5.16, 95% CI: 4.21–6.31), functional disability (aOR = 1.31, 95% CI: 1.05–1.63), rural residence (aOR = 1.26, 95% CI: 1.06–1.50), and basic labor occupations (aOR = 1.41, 95% CI: 1.13–1.78) predicted higher likelihood of experiencing extreme fatigue. Also, older adults of Gurma (aOR = 2.94, 95% CI: 2.05–4.19) and other ethnic groups (aOR = 1.73, 95% CI: 1.12–2.65) had higher odds of experiencing extreme fatigue. On the other hand, older adults in Northern (aOR = 0.31, 95% CI: 0.22–0.43) and Southern Ghana (aOR = 0.48, 95% CI: 0.40–0.58) were less likely to report extreme fatigue. Conclusion Chronic illness, functional disability, occupation, and regional disparities emerged as key predictors, underscoring the need for targeted health interventions such as tailored self-management education and Community-based exercise programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".