Cuba’s success in child health: what can one learn?
Bibliographic record
Abstract
Cuba has excellent child health as illustrated by its low child mortality rates. Child mortality rates (under 5 years, infant and neonatal) in Cuba have all been lower than in the USA for many years. WHO figures for 2016 for under 5 child mortality (U5M) show that Cuba has a U5M rate of 5.5 per 1000 live births, whereas the USA has a U5M rate of 6.5 and Costa Rica has a rate of 9.7.1 Cuba has the second-lowest U5M in the Americas behind Canada with a rate of 4.9. U5M is considered to be an excellent indicator of child health by UNICEF.2 Cuba is a middle-income country with considerable economic problems exacerbated by the blockade imposed by the USA. How then has it achieved such good child health outcomes? Cuba’s achievements in child health are due to a combination of factors.2 3 Cuba has an integrated healthcare system with all sections cooperating fully. Universal healthcare and universal education are the basis for good health. Literacy is at 99.7% and this enables public health campaigns to reach the entire population. Free universal education has resulted in Cuba having one of the highest doctor-to-population ratios. Programmes, such as ‘Educa a tu hijo’ (educate your child), are in place to prepare young children for school.4 This non-institutional-based programme was developed in rural areas, and subsequently extended throughout the country, as it was recognised that early child development is essential for child well-being. Primary healthcare is a key feature of healthcare in Cuba. Almost half of all Cuban doctors work in primary healthcare. Primary healthcare exists both in urban and remote rural areas. The presence of health facilities even in remote …
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".