Inequalities in health system coverage and quality: a cross-sectional survey of four Latin American countries
Bibliographic record
Abstract
The premise of health as a human right in Latin America has been challenged by health system fragmentation, quality gaps, a growing burden of chronic disease, sociopolitical upheaval, and the COVID-19 pandemic. We characterised inequities in health system quality in Colombia, Mexico, Peru, and Uruguay. We did a cross-sectional telephone survey with up to 1250 adults in each country. We created binary outcomes in coverage, user experience, system competence, and confidence in the system and calculated the slope index of inequality by income and education. Although access to care was high, only a third of respondents reported having a high-quality source of care and 25% of those with mental health needs had those needs met. Two-thirds of adults were able to access relevant preventive care and 42% of older adults were screened for cardiovascular disease. Telehealth access, communication and autonomy in most recent visit, reasonable waiting times, and receiving preventive health checks showed inequalities favouring people with a high income. In Uruguay, inequality between government and social security services explained a substantial proportion of disparities in preventive health access. In other study countries, inequalities were also substantial within government and social security subsectors. Essential health system functions are unequal in these four Latin American countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".