Caribbean Women Face Higher Obesity and Diabetes Amid Socioeconomic Struggles – A Cross-Sectional Study
Bibliographic record
Abstract
Abstract This cross-sectional study examined gender disparities in health outcomes across 30 Caribbean nations, focusing on the intersection of noncommunicable diseases (NCDs), reproductive health, and socioeconomic inequality. Using publicly available data from the Pan American Health Organization, United Nations Development Programme, and International Diabetes Federation, we first identified significantly higher obesity prevalence (43.88% vs. 29.77%) and a greater proportion of diabetes-related deaths among women (13.21% vs. 10.17%) in the Caribbean compared to men. We then compared regional outcomes to high-income neighbors in North America, selecting the U.S. and Canada as reference countries. The Caribbean showed substantially higher maternal mortality (9.39 vs. 1.41 per 10,000 live births) and infant mortality (11.58 vs. 5 per 1,000 live births), as well as lower Inequality-adjusted Human Development Index scores (0.556 vs. 0.839). Socioeconomic disparities were also pronounced, with higher rates of food insecurity (41.71% vs. 8.15%) and female unemployment (10.67% vs. 4.45%) in the Caribbean. Together, these findings reveal how interlocking health and social vulnerabilities disproportionately affect women in the region. Addressing these disparities will require coordinated policy action that extends beyond healthcare access to target the structural determinants driving gendered health inequities.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".