Social exclusion and universal health coverage: health care rights and citizen-led accountability in Guatemala and Peru
Bibliographic record
Abstract
BACKGROUND: While equity is a central concern in promoting Universal Health Coverage (UHC), the impact of social exclusion on equity in UHC remains underexplored. This paper examines challenges faced by socially excluded populations, with an emphasis on Indigenous peoples, to receive UHC in Latin America. We argue that social exclusion can have negative effects on health systems and can undermine progress towards UHC. We examine two case studies, one in Guatemala and one in Peru, involving citizen-led accountability initiatives that aim to identify and address problems with health care services for socially excluded groups. The case studies reveal how social exclusion can affect equity in UHC. METHODS: In-depth analysis was conducted of all peer reviewed articles published between 2015 and 2019 on the two cases (11 in total), and two non-peer reviewed reports published over the same period. In addition, two of the three authors contributed their first-hand knowledge gathered through practitioner involvement with the citizen-led initiatives examined in the two cases. The analysis sought to identify and compare challenges faced by socially excluded Indigenous populations to receive UHC in the two cases. RESULTS: Citizen-led accountability initiatives in Guatemala and Peru reveal very similar patterns of serious deficiencies that undermine efforts towards the realization of Universal Health Coverage in both countries. In each case, the socially excluded populations are served by a dysfunctional publicly provided health system marked by gaps and often invisible barriers. The cases suggest that, while funding and social rights to coverage have expanded, marginalized populations in Guatemala and Peru still do not receive either the health care services or the protection against financial hardship promised by health systems in each country. In both cases, the dysfunctional character of the system remains in place, undermining progress towards UHC. CONCLUSIONS: We conclude that efforts to promote UHC cannot stop at increasing health systems financing. In addition, these efforts need to contend with the deeper challenges of democratizing state institutions, including health systems, involved in marginalizing and excluding certain population groups. This includes stronger accountability systems within public institutions. More inclusive accountability mechanisms are an important step in promoting equitable progress towards UHC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".