Equity and the Cuban National Health System's response to COVID-19
Bibliographic record
Abstract
Cuba's National Health System has managed to guarantee an effective and equitable response to COVID-19. Universal and free health coverage, based on primary care, follows the principle of equity and the greatest resources are allocated to areas of the lowest socioeconomic stratum (where higher risk is concentrated), followed by those of medium and high strata, in that order. This allowed for similar mortality rates in the three strata, and Cuban national mortality rate was one of the lowest in the Region of the Americas. Before the first case was identified in Cuba, a Plan for Coronavirus Prevention and Control was elaborated with multisectoral participation, and when the first case was confirmed the Temporary National Working Group to Fight COVID-19 was created as an advisory body of the government. The actions to face the pandemic began with preventive measures in the community, continued in the isolation centers and ended again in the community with actions of surveillance and follow up of recovered patients. Following the principle of territoriality, molecular diagnosis laboratories were created in the provinces that did not have one. Free medical care and treatment; the preparation of a single national intersectoral government plan; the use of particular strategies for research, diagnosis and case tracing; and the implementation of a universal protocol for disease prevention and treatment of confirmed cases made it possible to control the disease with a health equity perspective.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".