Sociodemographic, functional disability and severe illness predict extreme fatigue among older adults in Ghana
Bibliographic record
Abstract
Abstract Background Extreme fatigue is a disabling but under-recognized condition among older adults. However, 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). Participants had a mean age of 59.9 ± 8.3 years (maximum age = 99 years), and the sample comprised 2,159 (44.63%) males and 2,679 (55.37%) females. Extreme fatigue was self-reported and recorded dichotomously (yes/no). 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, highlighting the need for geriatric care to extend beyond clinical treatment to include simple, community-level approaches that support daily functioning and healthy ageing, aligned with SDG 3.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".