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Record W3033236746 · doi:10.1142/9789811212413_0005

How Much Do Countries Spend on Primary Care in the Americas?

2020· book-chapter· en· W3033236746 on OpenAlexaboutno aff
Camilo Cid, Claudia Pescetto, James Fitzgerald, Amalia del Riego

Bibliographic record

VenueWorld Scientific series in global healthcare economics and public policy · 2020
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsPer capitaHealth carePublic expenditurePublic healthDeveloping countryEconomic growthGeographyBusinessDemographic economicsMedicineEnvironmental healthEconomicsPopulationPublic financeNursing

Abstract

fetched live from OpenAlex

The following sections are included:Estimating expenditure on the first level of care or primary care is not an easy task. There is a need to differentiate between primary health care (PHC) as an overarching approach to the organization and operation of health systems and the first level of care or primary care, which refers to a level of care in the provision of health services. Improved resolution capacity of the first level of care to expand access to comprehensive, quality health services is required to advance toward universal access to health and universal health coverage, hence the need to measure how much countries are spending in the first level of care.Results for 13 countries in the Americas chosen in this study indicate that the expenditure in the first level of care or primary care, as a percentage of public expenditure in health, presents a high variability, fluctuating between 12.5% in the United States and 44.2% in El Salvador. Despite the methodological differences found, we estimated a 24% median considering each country as one observation.When the indicator is compared to total health expenditure per capita for each country, two main groups and two outliers (Cuba and the US) are identified: the first (Bolivia, El Salvador, and Jamaica), with high spending in the first level of care as a percentage of total public expenditure in health (over 38%) but with low total expenditure per capita (less than int$550); the second (Argentina, Barbados, Brazil, Chile, Costa Rica, Mexico, and Uruguay), with spending in the first level of care as a percentage of total public expenditure in health (ranging 20–25%) with a high per capita expenditure (ranging 1, 000–2, 000 dollars).When comparing spending in the first level of care as a percentage of public expenditure in health, with the public expenditure in health as a percentage of GDP, three sets of countries are clearly identified: first, countries that invest between 20% and 25% in the first level of care and over 4% public expenditure in health as a percentage of GDP (Jamaica, Brazil, Chile, Costa Rica, Argentina, Uruguay, and Canada); countries with higher levels of investment in the first level of care, between 25% and 45%, but lower public expenditure in health as a percentage of GDP; and USA and Cuba are outliers, with low investment in first level of care in the first and very high investment in the second.Data found for other regions show a median of 16% of expenditure in the first level of care as a percentage of total public expenditure for the OECD countries, and for a sample of middle- and upper-middle-income countries, a median of 25% for spending in the first level of care as a percentage of total public expenditure.Our results show the need for a more comprehensive study and the promotion of an international definition of or first level of care and that further analysis is needed. For the case of countries in the Americas, there is a clear need also to standardize what is first level of care to allow for a more systematic monitoring and measurement that could be comparable across 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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.863
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.272
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

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Citations1
Published2020
Admission routes1
Has abstractyes

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