Factors associated with cognitive performance among urban older adults of Dodoma City, central Tanzania: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Cognitive performance among older adults globally shows varied trends, with high-income countries reporting improved outcomes due to enhanced education and healthcare. In contrast, sub-Saharan Africa faces rising rates of cognitive impairment, particularly among older people. Existing research in Tanzania has predominantly focused on rural populations, leaving a significant gap regarding urban older residents. This study addresses this gap by examining the factors that influence cognitive performance among older urban residents in Dodoma City. METHODS: We conducted an analytical cross-sectional study that included 435 older adults. Random sampling was applied to recruit study participants. Cognitive performance was assessed using the Montreal Cognitive Assessment (MoCA). Data were analysed using descriptive statistics and univariate and multivariate linear regression in SPSS version 29, with a statistical significance level set at p < 0.05 to determine cognitive performance and associated factors. RESULTS: The mean score on the cognitive performance test was 11.9, with a standard deviation of 7.564. Factors associated with cognitive performance were; Age, for every 9-year age difference (β: - 0.09; 95% CI - 0.15 to - 0.03), primary and secondary education (β: 2.73; 95% CI 1.51-3.94) and (β: 6.99; 95% CI 4.79-9.20) respectively. Self-employment (β: - 3.49; 95% CI - 5.05 to - 1.92), homemaker (β: - 4.91; 95% CI - 6.23 to - 3.56), unable to work (β: - 4.59; 95% CI - 6.26 to - 2.92), widowed participants (β: - 1.60; 95% CI - 2.70 to - 0.51), reported middle income (β: 7.29; 95% CI 1.59-12.79), Family size with fewer dependents (β: 2.82; 95% CI 1.73-3.90). Additionally, alcohol consumption (β: - 2.08; 95% CI - 3.27 to - 0.88), an increase in 6 units on geriatric depressive symptoms scores (β: - 0.15; 95% CI - 0.24 to - 0.05), and an increase in 2.5 units in IADL scores (β: 0.84; 95% CI 0.57-1.12). CONCLUSION: The findings indicate a complex interplay of demographic, educational, economic, and behavioural factors that significantly influence the low score in cognitive performance. The associated factors mentioned above should be addressed to increase cognitive performance. Overall, promoting educational and socioeconomic opportunities, along with addressing mental health issues, could play a crucial role in enhancing cognitive function in the aging population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".