Impact of informal caregiving on older adults’ physical and mental health in low-income and middle-income countries: a cross-sectional, secondary analysis based on the WHO’s Study on global AGEing and adult health (SAGE)
Bibliographic record
Abstract
OBJECTIVES: A high proportion of care stemming from chronic disease or disability in low-income and middle-income countries is provided by informal caregivers. The goal of this study was to determine the level of burden experienced by these caregivers, explore associated factors and assess whether caregivers' and non-caregivers' health differed. DESIGN AND SETTING: This cross-sectional study was a secondary analysis of data on caregivers' burden, health and health risk factors in Ghana, India and the Russian Federation collected as part of the WHO's Study on global AGEing and adult health (SAGE) Wave 1. PARTICIPANTS: Caregivers in Ghana (n=143), India (n=490) and Russia (n=270) completed the measures. OUTCOME MEASURES: Factors associated (ie, demographics and caregiving profile variables) with burden were explored among caregivers. Then, quality of life (QOL), perceived stress, depression, self-rated health (SRH) and health risk factors were compared between caregivers and matched non-caregivers (1:2). RESULTS: The largest caregiving subgroups were spouses and adult children. Caregivers mostly cared for one person and provided financial, social/emotional and/or physical support, but received little support themselves. Burden level ranged from 17.37 to 20.03. Variables associated with burden were mostly country-specific; however, some commonality for wealth, type of care and caregiving duration was noted. Caregivers with a moderate or high level of burden reported lower QOL and higher perceived stress than those experiencing low burden. Caregivers reported lower QOL and SRH than non-caregivers. CONCLUSION: Given the lack of support received and consequences of the burden endured by caregivers, policy and programme initiatives are needed to ensure that caregivers in low- and middle-income countries can fulfil their role without compromising their own health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".