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Record W4400580769 · doi:10.5935/2675-5602.20200416

Mapeamento das externalidades provocadas pelas recentes epidemias de dengue nas contas da Prefeitura Municipal de Ribeirão Preto

2024· article· pt· W4400580769 on OpenAlexaff
Rafaela Marcucci Silva, Luzia Márcia Romanholi Passos, Jennifer Midiani Gonella, Ariane Ranzani Rigotti, Aline Cristiane Cavicchioli Okido, Jacinthe Leclerc, Carlos Alberto Grespan Bonacim

Bibliographic record

VenueGlobal Academic Nursing Journal · 2024
Typearticle
Languagept
FieldSocial Sciences
TopicAcademic Research in Diverse Fields
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

The main was to map the externalities caused by the recent dengue epidemics in the accounts of the Municipality of Ribeirão Preto and estimate the partial costs related to the disease from the perspective of the SUS. This is a retrospective study, analyzing the costs related to dengue epidemics through the components: human resources, medicines, supplies, and hospitalizations related to the disease in the city of Ribeirão Preto and subsequent comparison between the epidemic years (2016 and 2019) and endemic diseases (2017 and 2018) and assessment of the economic and budgetary impact. The partial cost estimate and budget impact compared to the epidemic and endemic years was 15,484,446.83 BRL, the excess expenditure on NS1 kits in the epidemic years 2016 and 2019 was 142,549.97 BRL, epidemic years an average of 1,623% more was spent on hospital beds and indirect costs generated an additional cost of 354,624.24 BRL. This initial economic assessment provided information on the costs of the priority components related to dengue and, consequently, support for managers' decision-making and better planning of disease control and prevention activities, as well as guidance for better allocation of budgetary resources.

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)0.012
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0040.001
Research integrity0.0030.013
Insufficient payload (model declined to judge)0.0010.000

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 head0.091
GPT teacher head0.461
Teacher spread0.370 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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