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Record W7119314384

Comparative analysis of pharmaceutical services models adopted in countries with universal health coverage.

2019· dissertation· pt· W7119314384 on OpenAlexaboutno aff
Carolina Zampirolli Dias

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2019
Typedissertation
Languagept
FieldSocial Sciences
TopicPublic Health in Brazil
Canadian institutionsnot available
Fundersnot available
KeywordsUniversal designEssential medicinesHealth policyWork (physics)Right to healthHealth servicesAccess to medicinesPopulationDeveloping country
DOInot available

Abstract

fetched live from OpenAlex

Introduction: Brazil has a universal health system with basic principles of universal and equal access to health actions and services, including Pharmaceutical Services (PS). The entire Brazilian population has the right of access to medicines, selected and standardized through a List of Essential Medicines. Nevertheless, a survey assessed PS at the national level in 2015 and found that there are still major challenges in expanding and ensuring equitable access and service structuring nearly 30 years after the establishment of the Unified Health System (in portuguese Sistema Universal de Saúde - SUS). In this context, understanding and knowing the determining factors for the difficulties still faced by SUS in access to medicines is of fundamental importance, bringing a strategic and comprehensive view of the problem. The objective of this master thesis is to analyze the way PS in Brazil is organized, in perspective with the experiences and results of other countries with Universal Health Coverage (UHC). Methods: The methodology of this work was divided into three complementary parts. First, a review of general aspects of medicines policy in selected countries for knowledge of PS models in countries with UHC. For comparison, an adaptation of the health systems analysis model to medicines policy was performed, with a critical evaluation by experts in the field. Finally, the settings of the adapted method and their results in the selected countries were compared. Results: Information was collected about PS in seven countries: Canada, Australia, Scotland, Sweden, Portugal, South Africa and Colombia. After adaptation and critical evaluation by the experts, the comparison was performed. The parameters compared were: financing, payment, organization, regulation and persuasion. Among them, the biggest difference between the model adopted in Brazil and in other countries was found in the organization. Financing comes mostly through taxes in all countries, although some adopt copayment strategy in a complementary way. Regarding payment, responsibilities are decentralized in the provinces in most countries. The provinces, in turn, hire retail pharmacies to make the general population available. Thus, the acquisition and distribution logistics of medicines end up being the responsibility of the contracted services. In Brazil, however, the responsibilities are partially decentralized in the three federated entities, which purchase and dispensation of the drugs to the population. As for regulation, all countries have an agency for regulating the effectiveness and safety of medicines. In persuasion, strategies to improve the quality of drug use are a reality in the world, although some countries have not formalized them. Conclusion: The comparative analysis of PS showed some possible ways. However, assessing a potential change in public policy is not possible without analyzing its feasibility in the current contexto of the country.

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0060.008
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.042
GPT teacher head0.334
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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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Citations0
Published2019
Admission routes1
Has abstractyes

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