Social and Gender Inequalities in Depressive Symptoms Among Urban Older Adults of Latin America and the Caribbean
Bibliographic record
Abstract
OBJECTIVE: This study examined gender differences in depression by examining differential exposure and vulnerability to socioeconomic factors during the life course. METHODS: The data used for the analyses originated from a cross-national survey of older adults living in seven large Latin American cities. We examined associations between depressive symptomatology and socioeconomic conditions and health indicators in childhood, adulthood, and old age. We used the Geriatric Depression Scale to classify respondents with high levels of depressive symptoms. RESULT: The prevalence of depression in the urban population of Latin America was relatively low, ranging across cities from 0.4 to 5.2% in men and from 0.3 to 9.5% in women. Women were more exposed to social and material disadvantages during their life course than men but were not more vulnerable to them than men. Current socioeconomic conditions and health status as well as functional disabilities mainly accounted for gender differences in the prevalence of depression. Additionally, poor health and hunger during childhood, as well as illiteracy or lack of education, were associated with depression in both men and women. DISCUSSION: Cumulative life course exposure to social and material disadvantage and current material, social, and health conditions explain the higher frequency of depression in women.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".