Living Arrangements and the Role of Caregivers among the Elderly in Latin America
Bibliographic record
Abstract
Using the SABE(1) data set, this paper describes the support that the elderly receive from family members, siblings, friends and the community where they live in four Latin American cities. It also reports the activities that the elderly do for their family members. In the four distinct cities included in the study, we find similar trends in terms of living arrangements, the role of caregivers and the type of activities that elderly people provide for their family members. Our findings indicate the elderly without any support tend to be in better health and socio-economic conditions than elderly persons with family or community support; this is likely because healthier individuals need less assistance. Surprisingly, most of the elderly without any help from family members do not receive support from the community either. Daughters inside the household are the most likely caregivers and receive most assistance from the elderly in return. The exchange of services and activities within the household reflects the higher gains that female caregivers receive from taking care of elderly relatives, or the lower wages and consequently their lower cost of providing care. Among the providers of money, sons and daughters share similar characteristics. A significant number of caregivers are in the productive years of their life. A discussion of the policy options to increase elderly health and to improve the role of caregivers is included.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".