Geographical location as a determinant of caregiver burden: a rural-urban analysis of the informal caregiving, health, and healthcare survey in Ghana
Bibliographic record
Abstract
BACKGROUND: The caregiving scholarship widely acknowledges informal caregivers' contributions to maintaining older adults' health and well-being. However, informal caregivers encounter economic, physical, social, financial and psychological challenges when caring for older adults. The caregiving literature has shown variations in caregiving intensity and motivation between rural and urban informal caregivers of older adults. This situation is likely to result in rural-urban disparities in caregiver burden. However, the literature on predictors of caregiver burden is more focused on demographic, socio-economic, caregiving and health-related factors with very little attention to geographical dynamics. For this reason, the effects of demographic, socio-economic, caregiving, and health-related factors on the variations in caregiver burden between rural and urban informal caregivers of older adults are yet to be known in the sub-Saharan African context, including Ghana. Notably, the impact of geographical location on caregiver burden is mainly missing in the informal caregiving literature in Ghana. Situated within the stress process model, we determine the association between geographical location and caregiver burden among informal caregivers of older adults in Ghana. METHODS: This study employed data from a large cross-sectional survey on informal caregiving, health, and healthcare among caregivers of older adults aged 50 years or above (N = 1,853) in Ghana. We selected the World Health Organization Impact of Caregiving Scale to measure caregiver burden. Generalized multivariable linear regression models were employed to determine the association between geographical location and caregiver burden among informal caregivers of older adults. We reported beta values and standard errors with significance levels of 0.05 or less. RESULTS: The results showed that rural informal caregivers of older adults significantly have a decreased caregiver burden compared to urban informal caregivers (β = -1.64; SE = 0.41). Also, participants across all the self-rated health categories (poor/very poor: β = 12.63; SE = 1.65; fair: β = 9.56; SE = 1.07; good: β = 11.00; SE = 0.61, very good: β = 7.03; SE = 0.49) have a significantly increased caregiver burden for the full sample and for both rural (poor/very poor: β = 13.88; SE = 2.4; fair: β = 6.11; SE = 1.62; good: β = 9.97; SE = 0.96, very good: β = 6.06; SE = 0.71) and urban (poor/very poor: β = 11.86; SE = 2.25; fair: β = 12.33; SE = 1.42; good: β = 11.80; SE = 0.79, very good: β = 7.90; SE = 0.67) participants. This study further revealed that participants with no financial support needs reported a decreased caregiver burden compared to those with financial support needs for the full sample (β = -2.92, p-value < 0.01) and for both rural (β = -3.20; p-value < 0.01) and urban (β =-2.70; p-value < 0.01) participants. CONCLUSION: The findings from this study underscore geographical location differences in caregiver burden among informal caregivers of older adults in Ghana. Given these findings, the need to consider geographical location variations in providing welfare and health support programs to lessen caregiver burden among informal caregivers of older adults is welcomed. In line with the stress process model, such welfare and health programs should consider background, context, and stressor factors that contribute to variations in caregiver burden between rural and urban informal caregivers of older adults in Ghana and other sub-Saharan African countries.
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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 | 0.002 |
| 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) | 0.000 | 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".