Predictor of sleep difficulty among community dwelling older populations in 2 African settings
Bibliographic record
Abstract
Sleep deprivation is a common phenomenon among older population and is commonly linked to behavioral, physiological, and psychosocial factors. Not much is known about sleep deprivation among older population in Africa. Therefore, in this study we aimed to investigate the basic sociodemographic and psychosocial predictors of self-reported sleep deprivation among older population.In this study we analyzed cross-sectional data on 1495 community dwelling men and women aged 50 years and above. Data were collected from the SAGE Well-Being of Older People Study conducted in South Africa and Uganda. Outcome variable was self-reported sleep difficulty last 30 days. Multivariable logistic regression models were used to identify the variables significantly associated with sleep difficulty.The prevalence of mild-moderate sleep difficulty was 32.6% (27.9, 37.6) and severe/extreme 23.0% (20.3, 26.0) respectively. Multivariable analysis revealed that sleep difficulty was associated with several behavioral, environment, and illness conditions. In South Africa, those who reported dissatisfaction with living condition had 1.592 [1.087, 2.787] times higher odds of reporting mild/moderate sleep difficulty. Poor subjective quality of life (QoL) was associated with higher odds of severe/extreme sleep difficulties (odds ratios [OR] = 4.590, 95% confidence interval [CI] = 2.641, 7.977 for South Africa, and OR = 4.461, 95% CI = 2.048 and 9.716 for Uganda). In Uganda, perceived depression was associated with higher odds of severe/extreme (OR = 2.452, 95% CI = 1.073, 5.602) sleep difficulties among men, and both mild/moderate (OR = 1.717; 95% CI = 1.011, 2.914) and severe/extreme sleep difficulties among women (OR = 2.504, 95% CI = 1.408, 4.453).More than half of the participants had sleep difficulty of certain degrees, emphasising an urgent need for intervention for sleep deprivation in the population. Interventions targeting to promote subjective health, quality of life, and living environment may prove beneficial for improving sleep health in this regard.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".