Determinantes Sociais e Desigualdades em Saúde nas Américas e no Caribe: Scoping Review
Bibliographic record
Abstract
Background: Health inequalities in the Americas and the Caribbean are strongly influenced by Social Determinants of Health (SDOH), reflecting structural disparities that directly affect population well-being. Objective: To map the SDOH addressed in scientific literature from the region over the past decade, identifying patterns, challenges, and implications for health equity. Methodology: A scoping review was conducted following the Joanna Briggs Institute framework and PRISMA 2024 checklist, with protocol registered in the Open Science Framework. A total of 30 studies published between 2015 and 2024 were analyzed, covering nine countries. Results: The main SDOH identified were racial and residential segregation, food insecurity, mental health, access to healthcare, gender, education, immigration, and sanitation. The most represented countries were Brazil, the United States, and Canada. Conclusion: The findings show that inequalities related to race, education, housing, and mental health are critical determinants of health inequities. The mapping highlights the urgency of intersectoral policies and professional training strategies aligned with these challenges, supporting the development of more equitable and responsive health systems.
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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 | 0.016 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".